Project Description: Twitter US Airline Sentiment¶

Data Description:¶

A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").

Dataset:¶

The project is from a dataset from Kaggle. Link to the Kaggle project site: https://www.kaggle.com/crowdflower/twitter-airline-sentiment The dataset has to be downloaded from the above Kaggle website.

The dataset has the following columns:

tweet_id
airline_sentiment
airline_sentiment_confidence
negativereason
negativereason_confidence
airline
airline_sentiment_gold
name
negativereason_gold
retweet_count
text
tweet_coord
tweet_created
tweet_location
user_timezone

Objective:¶

To implement the techniques learnt as a part of the course.

Learning Outcomes:¶

Basic understanding of text pre-processing.
What to do after text pre-processing:
- Bag of words
- Tf-idf
Build the classification model.
Evaluate the Model.

Steps and tasks:¶

1. Import the libraries, load dataset, print shape of data, data description. (5 Marks)
1. Understand of data-columns: (5 Marks)
  - a. Drop all other columns except “text” and “airline_sentiment”.
  - b. Check the shape of data.
  - c. Print first 5 rows of data.
1. Text pre-processing: Data preparation. (20 Marks)
  - a. Html tag removal.
  - b. Tokenization.
  - c. Remove the numbers.
  - d. Removal of Special Characters and Punctuations.
  - e. Conversion to lowercase.
  - f. Lemmatize or stemming.
  - g. Join the words in the list to convert back to text string in the dataframe. (So that each row contains the data in text format.)
  - h. Print first 5 rows of data after pre-processing.
1. Vectorization: (10 Marks)
  - a. Use CountVectorizer.
  - b. Use TfidfVectorizer.
1. Fit and evaluate model using both type of vectorization. (6+6 Marks)
1. Summarize your understanding of the application of Various Pre-processing and Vectorization and performance of your model on this dataset. (8 Marks)

1) Import the libraries, load dataset, print shape of data, data description. (5 Marks)¶

import pandas as pd
import numpy as np

#Seaborn for visualization
import seaborn as sns

# Matplotlib for visualization
from matplotlib import pyplot as plt
# display plots in the notebook
%matplotlib inline

import re, string, unicodedata
from bs4 import BeautifulSoup

#Natural language processing tool-kit
import nltk

#!pip install contractions
import contractions

nltk.download('wordnet')
nltk.download('punkt')

from nltk.tokenize import word_tokenize 
from nltk.stem import LancasterStemmer, WordNetLemmatizer

#scikit-learn
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

from sklearn.ensemble import RandomForestClassifier
from mlxtend.plotting import plot_confusion_matrix
from imblearn.over_sampling import SMOTE

[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\deval\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\deval\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!

#load tweets Data Set
TW_DF = pd.read_csv('Tweets.csv')
TW_DF.head(15)

#Show last 15 records
TW_DF.tail(15)

#Print shape of data
TW_DF.shape

(14640, 15)

TW_DF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14640 entries, 0 to 14639
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   tweet_id                      14640 non-null  int64  
 1   airline_sentiment             14640 non-null  object 
 2   airline_sentiment_confidence  14640 non-null  float64
 3   negativereason                9178 non-null   object 
 4   negativereason_confidence     10522 non-null  float64
 5   airline                       14640 non-null  object 
 6   airline_sentiment_gold        40 non-null     object 
 7   name                          14640 non-null  object 
 8   negativereason_gold           32 non-null     object 
 9   retweet_count                 14640 non-null  int64  
 10  text                          14640 non-null  object 
 11  tweet_coord                   1019 non-null   object 
 12  tweet_created                 14640 non-null  object 
 13  tweet_location                9907 non-null   object 
 14  user_timezone                 9820 non-null   object 
dtypes: float64(2), int64(2), object(11)
memory usage: 1.7+ MB

#Data Description
TW_DF.describe()

#Check Null values
TW_DF.isnull().sum()

tweet_id                            0
airline_sentiment                   0
airline_sentiment_confidence        0
negativereason                   5462
negativereason_confidence        4118
airline                             0
airline_sentiment_gold          14600
name                                0
negativereason_gold             14608
retweet_count                       0
text                                0
tweet_coord                     13621
tweet_created                       0
tweet_location                   4733
user_timezone                    4820
dtype: int64

#Check NA values
TW_DF.isna().sum()

tweet_id                            0
airline_sentiment                   0
airline_sentiment_confidence        0
negativereason                   5462
negativereason_confidence        4118
airline                             0
airline_sentiment_gold          14600
name                                0
negativereason_gold             14608
retweet_count                       0
text                                0
tweet_coord                     13621
tweet_created                       0
tweet_location                   4733
user_timezone                    4820
dtype: int64

#Number of unique values in each column
TW_DF.nunique()

tweet_id                        14485
airline_sentiment                   3
airline_sentiment_confidence     1023
negativereason                     10
negativereason_confidence        1410
airline                             6
airline_sentiment_gold              3
name                             7701
negativereason_gold                13
retweet_count                      18
text                            14427
tweet_coord                       832
tweet_created                   14247
tweet_location                   3081
user_timezone                      85
dtype: int64

#Check if tweet data set has duplicate records
TW_DF_Duplicate = TW_DF[TW_DF.duplicated()]
TW_DF_Duplicate.head(15)

#There are some duplicate records, that first needs to be taken care of.
TW_DF_Duplicate.shape

(36, 15)

#Remove duplicate values
TW_DF.drop_duplicates(keep=False,inplace=True)

#Print shape of data after removing duplicate values
TW_DF.shape

(14568, 15)

#Data Description after removing duplicate values
TW_DF.describe()

2) Understand of data-columns: (5 Marks)¶

#List the all columns available in the tweet data set.
TW_DF.columns

Index(['tweet_id', 'airline_sentiment', 'airline_sentiment_confidence',
       'negativereason', 'negativereason_confidence', 'airline',
       'airline_sentiment_gold', 'name', 'negativereason_gold',
       'retweet_count', 'text', 'tweet_coord', 'tweet_created',
       'tweet_location', 'user_timezone'],
      dtype='object')

#Drop all other columns except “text” and “airline_sentiment”
TW_DF_Update = TW_DF.drop(['tweet_id', 'airline_sentiment_confidence',
       'negativereason', 'negativereason_confidence', 'airline',
       'airline_sentiment_gold', 'name', 'negativereason_gold',
       'retweet_count', 'tweet_coord', 'tweet_created',
       'tweet_location', 'user_timezone'], axis =1)

#Check the shape of data
TW_DF_Update.shape

(14568, 2)

#Print first 5 rows of tweet data set.
TW_DF_Update.head(5)

3) Text pre-processing: Data preparation. (20 Marks)¶

#HTML tag removal function
def html_Remove(data_param):  
  review_text = BeautifulSoup(data_param).get_text()
  return review_text

#Tokenization function
def tokenization_Perform(data_param):  
  word_tokens = word_tokenize(data_param)
  return word_tokens

#Numbers Removal function
def number_Remove(list_of_words): 
    pattern = '[0-9]'
    list = [re.sub(pattern, '', i) for i in list_of_words] 
    return list

#Removal of Special Characters and Punctuations functions
def special_character_Remove(lst_of_words): 
    pattern = '[^A-Za-z0-9]+'
    list = [re.sub(pattern, '', i) for i in lst_of_words] 
    return list

def punctuation_Remove(words):
    """Remove punctuation from list of tokenized words"""
    new_words = []                        # Create empty list to store pre-processed words.
    for word in words:
        new_word = re.sub(r'[^\w\s]', '', word)
        if new_word != '':
            new_words.append(new_word)    # Append processed words to new list.
    return new_words

#Conversion to lowercase function
def lowercase_Convert(words):
    """Convert all characters to lowercase from list of tokenized words"""
    new_words = []                        
    for word in words:
        new_word = word.lower()           
        new_words.append(new_word)        
    return new_words

#Lemmatize and stemming functions

def stem_words(words):
    """Stem words"""
    stemmer = LancasterStemmer()
    stems = []                            
    for word in words:
        stem = stemmer.stem(word)
        stems.append(stem)                
    return stems

#or

def lemmatize_words(words):
    """Lemmatize words"""
    lemmatizer = WordNetLemmatizer()
    lemmas = []                           
    for word in words:
        lemma = lemmatizer.lemmatize(word, pos='v')
        lemmas.append(lemma)              
    return lemmas

#Remove empty String function
def empty_str_Remove(words):
  return list(filter(None, words))

#Replace Contraction function
def contractions_Replace(data_param):
    #Replace contractions in data_param
    return contractions.fix(data_param)

#Pre-Processing function
def pre_Processing_Func(data_param):
    inner_Pre_Processing = html_Remove(data_param)
    inner_Pre_Processing = contractions_Replace(inner_Pre_Processing)
    inner_Pre_Processing = tokenization_Perform(inner_Pre_Processing)
    inner_Pre_Processing = number_Remove(inner_Pre_Processing)
    inner_Pre_Processing = special_character_Remove(inner_Pre_Processing)
    inner_Pre_Processing = punctuation_Remove(inner_Pre_Processing)
    inner_Pre_Processing = lowercase_Convert(inner_Pre_Processing)
    inner_Pre_Processing = empty_str_Remove(inner_Pre_Processing)
    inner_Pre_Processing = lemmatize_words(inner_Pre_Processing) #clean_review = stem_words(clean_review)
    return inner_Pre_Processing

#Check before pre-processing
TW_DF_Update.head()

#Join the words in the list to convert back to text string in the dataframe. (So that each row contains the data in text format.)

pre_Processing_Tweets = []

for i, row in TW_DF_Update.iterrows():
    words = TW_DF_Update.at[i, 'text']
    words = pre_Processing_Func(words)
    TW_DF_Update.at[i,'text'] = " ".join( words )
    pre_Processing_Tweets.append( TW_DF_Update.at[i, 'text'] )
TW_DF_Update.head(50)
TW_DF_Update.style.set_properties(subset=['text'], **{'width': '400px'})

#Print first 5 rows of data after pre-processing.
TW_DF_Update.head(5)

4) Vectorization: (10 Marks)¶

##Use CountVectorizer
vectorizer_count_DF = CountVectorizer(analyzer="word",tokenizer = None,preprocessor = None, stop_words = None,max_features = 5000) 
vectorizer_features_DF = vectorizer_count_DF.fit_transform(pre_Processing_Tweets)
vectorizer_features_DF = vectorizer_features_DF.toarray()

print (vectorizer_features_DF.shape)
print(vectorizer_features_DF)

(14568, 5000)
[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]

#Take a look at the words in the vocabulary
vectorizer_vocab_DF = vectorizer_count_DF.get_feature_names()
print (vectorizer_vocab_DF)

['aa', 'aacom', 'aadv', 'aadvantage', 'aafail', 'aal', 'aano', 'aarp', 'abandon', 'abc', 'abcnetwork', 'abilities', 'ability', 'able', 'aboard', 'about', 'above', 'abq', 'abroad', 'absolute', 'absolutely', 'absurd', 'abt', 'abuse', 'abysmal', 'ac', 'accept', 'acceptable', 'access', 'accident', 'accidentally', 'accommodate', 'accommodations', 'accomplish', 'accord', 'accordingly', 'account', 'accountability', 'accrue', 'acct', 'accts', 'accurate', 'accuse', 'achieve', 'acknowledge', 'acknowledgement', 'acknowledgment', 'acquire', 'acquisition', 'across', 'act', 'action', 'activate', 'active', 'activitiesprevention', 'actual', 'actually', 'ad', 'adam', 'add', 'addition', 'additional', 'address', 'addtl', 'adjacent', 'admiral', 'admirals', 'admit', 'adopt', 'adore', 'ads', 'adult', 'advance', 'advantage', 'advertise', 'advice', 'advise', 'advisories', 'advisory', 'aerojobmarket', 'affect', 'affiliate', 'afford', 'affordable', 'afraid', 'after', 'afterall', 'afternoon', 'again', 'against', 'age', 'agency', 'agent', 'agents', 'agentthen', 'agentwhat', 'aggravate', 'aggressive', 'agnt', 'ago', 'agostill', 'agree', 'agt', 'ah', 'ahead', 'ahhhh', 'ahold', 'ail', 'aim', 'air', 'airborne', 'airbus', 'aircanada', 'aircraft', 'airfare', 'airline', 'airlinegeeks', 'airlines', 'airnzusa', 'airplane', 'airplanes', 'airport', 'airports', 'airway', 'airways', 'aisle', 'aka', 'al', 'alas', 'alaska', 'alaskaair', 'albany', 'albuquerque', 'alcohol', 'alert', 'alex', 'ali', 'alist', 'alive', 'all', 'allende', 'allergic', 'allergies', 'allergy', 'alleviate', 'alliance', 'allow', 'allowance', 'almost', 'alone', 'along', 'alot', 'already', 'alright', 'also', 'alt', 'alternate', 'alternative', 'alternatives', 'although', 'altitude', 'always', 'alwayslate', 'amateur', 'amateurish', 'amaze', 'amazingly', 'amazon', 'america', 'american', 'americanair', 'americanairlines', 'americanairlinesfail', 'americanview', 'amex', 'amm', 'among', 'amongst', 'amount', 'ams', 'amsterdam', 'amybruni', 'an', 'ana', 'anamarketers', 'anchorage', 'and', 'andor', 'andrew', 'andrews', 'andrewwasila', 'android', 'angel', 'angeles', 'angry', 'angrycustomer', 'anku', 'ann', 'annettenaif', 'anniversary', 'announce', 'announcement', 'announcements', 'annoy', 'annricord', 'annual', 'another', 'answer', 'answerthephone', 'anthony', 'anticipate', 'antigua', 'antonio', 'anxiety', 'anxious', 'any', 'anybody', 'anymore', 'anyone', 'anything', 'anytime', 'anyway', 'anywhere', 'ap', 'apart', 'apologies', 'apologise', 'apologize', 'apology', 'app', 'appal', 'apparent', 'apparently', 'appear', 'appease', 'applaud', 'apple', 'applepay', 'applicable', 'application', 'apply', 'appointments', 'appreciate', 'appreciation', 'approach', 'appropriate', 'approve', 'approx', 'apps', 'apr', 'april', 'apt', 'arbitrary', 'ardent', 'area', 'argentina', 'argue', 'argument', 'arizona', 'arkansas', 'arm', 'armrest', 'army', 'around', 'arrange', 'arrangements', 'arrival', 'arrivals', 'arrive', 'arrogant', 'article', 'aruba', 'as', 'ase', 'ashamed', 'ashley', 'asia', 'aside', 'ask', 'askpaypal', 'asleep', 'aspen', 'ass', 'assault', 'asset', 'assets', 'asshole', 'assholes', 'assign', 'assignment', 'assignments', 'assist', 'assistance', 'associate', 'assult', 'assume', 'assure', 'astound', 'at', 'atc', 'ath', 'athlete', 'athletes', 'atl', 'atlanta', 'atlantic', 'atleast', 'atrocious', 'attach', 'attempt', 'attend', 'attendant', 'attendants', 'attendees', 'attention', 'attentiveness', 'attitude', 'attitudes', 'atx', 'au', 'aug', 'august', 'auh', 'aus', 'austin', 'austinairport', 'australia', 'austrian', 'auto', 'automate', 'automatic', 'automatically', 'autoresponse', 'avail', 'availability', 'available', 'average', 'avert', 'avgeek', 'aviation', 'avis', 'aviv', 'avoid', 'avon', 'aw', 'await', 'awake', 'award', 'aware', 'away', 'awe', 'awesome', 'awesomeness', 'awful', 'awhile', 'awkward', 'aww', 'awww', 'az', 'ba', 'baby', 'back', 'backlog', 'backpack', 'backroads', 'backup', 'backwards', 'bad', 'badbusiness', 'badcustomerservice', 'badge', 'badly', 'badmgmt', 'badpolicy', 'badservice', 'bae', 'bag', 'bagage', 'baggage', 'baggagelost', 'baggages', 'bahamas', 'bait', 'bake', 'balance', 'ball', 'baltimore', 'ban', 'band', 'bandwidth', 'bangkok', 'bank', 'bankrupt', 'bar', 'barbados', 'barbara', 'barely', 'base', 'basic', 'basically', 'basket', 'bat', 'bathroom', 'bathrooms', 'batteries', 'battery', 'battierccipuppy', 'battle', 'bay', 'bc', 'bday', 'bdl', 'be', 'beach', 'beamske', 'beanie', 'beantownmatty', 'bear', 'beat', 'beatsmusic', 'beautiful', 'beautifully', 'beauty', 'because', 'become', 'bed', 'beer', 'beers', 'before', 'beforeafter', 'beg', 'begin', 'behalf', 'behave', 'behavior', 'behind', 'bein', 'belfast', 'believe', 'belize', 'bellagio', 'belong', 'below', 'belt', 'bench', 'beneficial', 'benefit', 'ber', 'bereavement', 'besides', 'best', 'bestairline', 'bestairlineever', 'bestemployees', 'bestflightever', 'bestfriends', 'besty', 'bet', 'betsy', 'better', 'between', 'beverage', 'beverages', 'beware', 'beyond', 'bf', 'bff', 'bgm', 'bgr', 'bhm', 'bicycle', 'big', 'bigger', 'biggest', 'biggie', 'bike', 'bill', 'billions', 'bin', 'bind', 'bio', 'bird', 'birmingham', 'birth', 'birthdate', 'birthday', 'bitch', 'bite', 'biz', 'bizarre', 'bk', 'bkk', 'black', 'blackhistorymonth', 'blacklist', 'blah', 'blame', 'blank', 'blanket', 'blast', 'blatant', 'bless', 'blizzard', 'block', 'blog', 'bloody', 'blow', 'blue', 'bluecarpet', 'bluemanity', 'blumanity', 'bna', 'board', 'boat', 'boeing', 'boeingairplanes', 'bogota', 'bohol', 'boise', 'bold', 'bom', 'bonus', 'bonuses', 'boo', 'book', 'boom', 'boooo', 'boot', 'booze', 'bora', 'border', 'bos', 'boss', 'boston', 'bostonlogan', 'bot', 'both', 'bother', 'bots', 'bottle', 'bottom', 'bougth', 'bounce', 'bourbon', 'bout', 'box', 'boy', 'boycott', 'boyfriend', 'boys', 'bpdfpp', 'bqn', 'brag', 'brain', 'brand', 'brandloveaffair', 'brandmance', 'brandssayingbae', 'branson', 'brave', 'bravo', 'brazil', 'brd', 'breach', 'break', 'breakdown', 'breakfast', 'breast', 'breath', 'breathe', 'breeze', 'bretharold', 'brian', 'bridge', 'bright', 'brilliant', 'bring', 'britishairways', 'bro', 'broadway', 'brokenpromises', 'brother', 'brothers', 'browser', 'browsers', 'bruh', 'brush', 'brutal', 'bs', 'btr', 'bttr', 'btv', 'btw', 'btwn', 'buck', 'bucket', 'buddies', 'buddy', 'budget', 'buf', 'buffalo', 'bug', 'build', 'bulkhead', 'bull', 'bullshit', 'bully', 'bum', 'bummer', 'bump', 'bumper', 'bunch', 'burbank', 'burger', 'burn', 'burningman', 'bus', 'bush', 'busiest', 'business', 'businessfirst', 'bussey', 'busy', 'but', 'butt', 'button', 'buttonthatcalled', 'buy', 'buyer', 'bwi', 'bwiairport', 'bwisjd', 'by', 'bye', 'ca', 'cab', 'cabin', 'cabo', 'cache', 'cae', 'caffeine', 'cake', 'cal', 'calendar', 'calgary', 'cali', 'california', 'call', 'callback', 'callbacks', 'caller', 'callers', 'camera', 'camp', 'campilley', 'can', 'cana', 'canada', 'canadian', 'cancel', 'cancer', 'cancun', 'canx', 'cap', 'capaaviation', 'capable', 'capacity', 'capital', 'capt', 'captain', 'captive', 'capture', 'car', 'card', 'cardholder', 'care', 'career', 'cargo', 'caribbean', 'carol', 'carolina', 'carousel', 'carpet', 'carrie', 'carrier', 'carriers', 'carrieunderwood', 'carry', 'carryon', 'carryons', 'carseat', 'carsl', 'cart', 'case', 'cash', 'casimir', 'cat', 'catch', 'cater', 'cattle', 'cause', 'cb', 'cc', 'cdg', 'cebu', 'celebrate', 'cell', 'cellphone', 'center', 'central', 'centre', 'centric', 'centricity', 'cents', 'century', 'centuryca', 'ceo', 'cert', 'certain', 'certainly', 'certificate', 'cessna', 'cesspool', 'cflanagian', 'cha', 'chain', 'chair', 'chairman', 'challenge', 'chance', 'change', 'channel', 'chaos', 'chaotic', 'chapter', 'char', 'charac', 'character', 'characterize', 'charge', 'charity', 'charles', 'charleston', 'charlotte', 'charm', 'chart', 'charter', 'chase', 'chasefoster', 'chat', 'cheap', 'cheaper', 'cheapflights', 'cheat', 'check', 'checkin', 'checkins', 'checkout', 'checkpoint', 'cheer', 'cheertymedad', 'cheese', 'cheesy', 'chef', 'cherry', 'chg', 'chi', 'chicago', 'chicagotribune', 'chicken', 'chief', 'child', 'childish', 'children', 'chill', 'chimid', 'china', 'chip', 'chk', 'chkd', 'chnge', 'cho', 'chocolate', 'choice', 'choices', 'choose', 'choppiest', 'christmas', 'chrome', 'chronicleherald', 'chs', 'cincinnati', 'cincy', 'circle', 'circumstances', 'cite', 'cities', 'citizen', 'city', 'claim', 'clarence', 'clarification', 'clarify', 'clarkey', 'class', 'classy', 'claudoakeshott', 'cld', 'cldnt', 'cle', 'clean', 'clear', 'clearly', 'clesfo', 'cleveland', 'clever', 'click', 'client', 'clients', 'cll', 'clockwork', 'close', 'closepwcs', 'closer', 'closest', 'clothe', 'cloud', 'clown', 'clt', 'club', 'clue', 'clueless', 'cluster', 'clutch', 'cmh', 'cmon', 'cnn', 'cnnbrk', 'cnnmoney', 'cnx', 'cnxn', 'co', 'coach', 'coast', 'coat', 'cockpit', 'cocktail', 'cod', 'code', 'codeshare', 'coffee', 'coincidence', 'coke', 'cold', 'colleague', 'colleagues', 'collect', 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'upset', 'upstate', 'ur', 'urgency', 'urgent', 'urgently', 'us', 'usa', 'usaa', 'usair', 'usairsucks', 'usairways', 'usairwayscom', 'usairwaysfail', 'usairwayssuck', 'usairwayssucks', 'usatoday', 'use', 'useful', 'useless', 'user', 'username', 'usual', 'usually', 'utah', 'utterly', 'uvf', 'uxzj', 'va', 'vaca', 'vacation', 'vacay', 'valentine', 'valentines', 'valid', 'vallarta', 'valuable', 'value', 'van', 'various', 'veer', 'vegan', 'vegas', 'velourlive', 'vent', 'verbal', 'verify', 'version', 'versus', 'very', 'vet', 'vetr', 'via', 'vibe', 'vice', 'vicky', 'video', 'videos', 'vieques', 'view', 'vile', 'vincenzolandino', 'vineyard', 'vinyl', 'vinylvegas', 'violation', 'virgin', 'virginamerica', 'virginatlantic', 'visa', 'visible', 'visit', 'vital', 'vm', 'voice', 'voicemail', 'void', 'volume', 'volumes', 'volunteer', 'vomit', 'vote', 'voucher', 'vouchers', 'vp', 'vs', 'vx', 'wa', 'waa', 'wait', 'waitin', 'waitlisted', 'waive', 'waiver', 'waivers', 'waivethefee', 'wake', 'walk', 'wall', 'wallet', 'wander', 'wannagohome', 'want', 'ward', 'warehouse', 'warm', 'warmer', 'warmth', 'warn', 'warriors', 'washington', 'waste', 'wastedtime', 'wasteoftime', 'wat', 'watch', 'water', 'waterbury', 'wave', 'way', 'wayne', 'ways', 'wc', 'wcancelled', 'wd', 'wdelays', 'we', 'weak', 'wear', 'weary', 'weather', 'weatherrelate', 'weave', 'web', 'website', 'websites', 'wed', 'wednesday', 'week', 'weekend', 'weeks', 'weigh', 'weight', 'weird', 'welcome', 'well', 'welldone', 'west', 'westchester', 'western', 'westjet', 'wet', 'whammie', 'what', 'whatever', 'wheel', 'wheelchair', 'wheelchairs', 'wheelsup', 'when', 'whenever', 'where', 'whether', 'which', 'while', 'whiplash', 'whisper', 'white', 'who', 'whoa', 'whoever', 'whole', 'whose', 'wht', 'why', 'wichita', 'wide', 'wife', 'wifi', 'will', 'win', 'wind', 'window', 'windows', 'wine', 'wing', 'winner', 'winners', 'winter', 'winterstorm', 'winterweather', 'wipe', 'wire', 'wish', 'with', 'within', 'without', 'witness', 'wk', 'wknd', 'wkrb', 'wks', 'wld', 'wmcactionnews', 'wmy', 'wn', 'wno', 'woaw', 'wof', 'woman', 'women', 'wonder', 'wonderful', 'woo', 'woof', 'woohoo', 'word', 'work', 'workaround', 'worker', 'workers', 'world', 'worry', 'worse', 'worst', 'worstairline', 'worstairlineever', 'worstcustomerservice', 'worstcustomerserviceever', 'worstflight', 'worstflightever', 'worstunitedflightsever', 'worth', 'worthless', 'would', 'wouldbe', 'wound', 'wour', 'wout', 'wow', 'wrap', 'wreck', 'write', 'wrong', 'wrongfully', 'wsj', 'wtf', 'wth', 'wudda', 'wut', 'wx', 'wyou', 'xdaily', 'xday', 'xna', 'xoxo', 'xt', 'xweekly', 'xx', 'ya', 'yards', 'yay', 'yea', 'yeah', 'year', 'yearly', 'years', 'yell', 'yep', 'yes', 'yest', 'yesterday', 'yet', 'yikes', 'yo', 'yof', 'york', 'you', 'youareonyourown', 'youk', 'young', 'younger', 'your', 'yours', 'yourself', 'yourselves', 'yous', 'yousuck', 'youth', 'yow', 'yr', 'yrs', 'yuck', 'yuma', 'yummy', 'yup', 'yvonne', 'yvr', 'yyz', 'zero', 'zkatcher', 'zone', 'zoom', 'zurich']

#Stop words
vectorizer_stop_words_DF = vectorizer_count_DF.get_stop_words()
print ('Stop Words present:', vectorizer_stop_words_DF)

Stop Words present: None

#Sum of each word vocabulary
dist_DF = np.sum(vectorizer_features_DF, axis=0)
for tag, count in zip(vectorizer_vocab_DF, dist_DF):
    print (count, tag)

274 aa
3 aacom
2 aadv
10 aadvantage
2 aafail
2 aal
2 aano
3 aarp
3 abandon
8 abc
3 abcnetwork
2 abilities
5 ability
119 able
4 aboard
509 about
18 above
9 abq
2 abroad
22 absolute
30 absolutely
13 absurd
7 abt
3 abuse
6 abysmal
9 ac
32 accept
23 acceptable
39 access
5 accident
4 accidentally
26 accommodate
8 accommodations
4 accomplish
11 accord
4 accordingly
80 account
6 accountability
2 accrue
10 acct
2 accts
10 accurate
2 accuse
3 achieve
4 acknowledge
2 acknowledgement
2 acknowledgment
2 acquire
2 acquisition
16 across
18 act
13 action
2 activate
4 active
2 activitiesprevention
21 actual
86 actually
4 ad
2 adam
126 add
4 addition
17 additional
52 address
3 addtl
2 adjacent
5 admiral
12 admirals
6 admit
6 adopt
2 adore
5 ads
2 adult
14 advance
15 advantage
11 advertise
9 advice
20 advise
2 advisories
24 advisory
2 aerojobmarket
15 affect
3 affiliate
13 afford
3 affordable
2 afraid
467 after
2 afterall
30 afternoon
391 again
6 against
15 age
6 agency
257 agent
155 agents
1 agentthen
1 agentwhat
3 aggravate
3 aggressive
2 agnt
131 ago
1 agostill
9 agree
3 agt
12 ah
17 ahead
4 ahhhh
8 ahold
2 ail
2 aim
143 air
2 airborne
8 airbus
7 aircanada
32 aircraft
11 airfare
396 airline
3 airlinegeeks
156 airlines
2 airnzusa
23 airplane
4 airplanes
343 airport
23 airports
2 airway
87 airways
5 aisle
5 aka
2 al
2 alas
5 alaska
2 alaskaair
6 albany
4 albuquerque
2 alcohol
18 alert
3 alex
4 ali
11 alist
4 alive
695 all
2 allende
2 allergic
2 allergies
2 allergy
2 alleviate
9 alliance
88 allow
4 allowance
93 almost
18 alone
18 along
6 alot
177 already
9 alright
133 also
2 alt
13 alternate
3 alternative
4 alternatives
13 although
3 altitude
106 always
2 alwayslate
2 amateur
2 amateurish
103 amaze
2 amazingly
2 amazon
20 america
76 american
2889 americanair
27 americanairlines
2 americanairlinesfail
5 americanview
4 amex
2 amm
11 among
2 amongst
17 amount
2 ams
2 amsterdam
3 amybruni
979 an
4 ana
4 anamarketers
2 anchorage
3709 and
5 andor
2 andrew
3 andrews
2 andrewwasila
3 android
3 angel
2 angeles
20 angry
2 angrycustomer
2 anku
2 ann
2 annettenaif
8 anniversary
29 announce
15 announcement
7 announcements
19 annoy
5 annricord
5 annual
271 another
190 answer
2 answerthephone
4 anthony
6 anticipate
2 antigua
5 antonio
2 anxiety
5 anxious
441 any
4 anybody
27 anymore
114 anyone
136 anything
9 anytime
28 anyway
27 anywhere
2 ap
9 apart
17 apologies
2 apologise
21 apologize
48 apology
109 app
7 appal
3 apparent
38 apparently
22 appear
19 appease
2 applaud
9 apple
2 applepay
2 applicable
5 application
30 apply
3 appointments
159 appreciate
3 appreciation
10 approach
5 appropriate
8 approve
3 approx
4 apps
2 apr
14 april
3 apt
2 arbitrary
2 ardent
30 area
4 argentina
8 argue
3 argument
4 arizona
2 arkansas
3 arm
4 armrest
2 army
63 around
8 arrange
6 arrangements
31 arrival
3 arrivals
155 arrive
3 arrogant
8 article
4 aruba
572 as
4 ase
10 ashamed
2 ashley
2 asia
2 aside
165 ask
4 askpaypal
6 asleep
7 aspen
10 ass
5 assault
2 asset
2 assets
2 asshole
2 assholes
19 assign
9 assignment
3 assignments
27 assist
53 assistance
2 associate
5 assult
12 assume
7 assure
4 astound
1488 at
4 atc
2 ath
3 athlete
2 athletes
38 atl
37 atlanta
6 atlantic
2 atleast
7 atrocious
5 attach
28 attempt
10 attend
90 attendant
57 attendants
2 attendees
10 attention
2 attentiveness
29 attitude
3 attitudes
3 atx
2 au
7 aug
13 august
3 auh
12 aus
35 austin
3 austinairport
10 australia
3 austrian
18 auto
40 automate
2 automatic
14 automatically
2 autoresponse
16 avail
8 availability
109 available
5 average
2 avert
22 avgeek
10 aviation
2 avis
5 aviv
18 avoid
2 avon
3 aw
7 await
3 awake
52 award
13 aware
74 away
5 awe
119 awesome
2 awesomeness
60 awful
2 awhile
4 awkward
5 aww
3 awww
4 az
11 ba
32 baby
526 back
3 backlog
3 backpack
2 backroads
6 backup
2 backwards
182 bad
2 badbusiness
18 badcustomerservice
7 badge
9 badly
3 badmgmt
2 badpolicy
21 badservice
6 bae
756 bag
4 bagage
227 baggage
2 baggagelost
2 baggages
5 bahamas
2 bait
2 bake
11 balance
16 ball
7 baltimore
2 ban
9 band
2 bandwidth
2 bangkok
24 bank
5 bankrupt
7 bar
5 barbados
2 barbara
7 barely
20 base
6 basic
11 basically
3 basket
3 bat
9 bathroom
3 bathrooms
2 batteries
4 battery
2 battierccipuppy
21 battle
8 bay
97 bc
16 bday
12 bdl
11041 be
12 beach
3 beamske
2 beanie
2 beantownmatty
3 bear
15 beat
2 beatsmusic
14 beautiful
2 beautifully
5 beauty
355 because
21 become
4 bed
7 beer
2 beers
206 before
2 beforeafter
4 beg
24 begin
2 behalf
3 behave
4 behavior
26 behind
2 bein
2 belfast
58 believe
5 belize
2 bellagio
9 belong
6 below
6 belt
2 bench
2 beneficial
12 benefit
6 ber
5 bereavement
6 besides
191 best
4 bestairline
2 bestairlineever
2 bestemployees
2 bestflightever
2 bestfriends
2 besty
15 bet
2 betsy
182 better
45 between
2 beverage
2 beverages
3 beware
41 beyond
9 bf
2 bff
2 bgm
3 bgr
6 bhm
2 bicycle
71 big
2 bigger
9 biggest
2 biggie
4 bike
11 bill
3 billions
18 bin
4 bind
2 bio
21 bird
7 birmingham
5 birth
2 birthdate
31 birthday
3 bitch
30 bite
12 biz
2 bizarre
3 bk
3 bkk
12 black
2 blackhistorymonth
2 blacklist
6 blah
28 blame
3 blank
4 blanket
6 blast
3 blatant
3 bless
2 blizzard
7 block
9 blog
4 bloody
11 blow
46 blue
2 bluecarpet
8 bluemanity
2 blumanity
29 bna
351 board
3 boat
4 boeing
2 boeingairplanes
5 bogota
2 bohol
4 boise
2 bold
2 bom
9 bonus
2 bonuses
5 boo
500 book
2 boom
2 boooo
5 boot
3 booze
2 bora
3 border
79 bos
5 boss
85 boston
6 bostonlogan
3 bot
71 both
25 bother
2 bots
8 bottle
4 bottom
3 bougth
5 bounce
2 bourbon
3 bout
5 box
5 boy
3 boycott
8 boyfriend
4 boys
1 bpdfpp
2 bqn
3 brag
4 brain
16 brand
4 brandloveaffair
5 brandmance
2 brandssayingbae
1 branson
3 brave
2 bravo
2 brazil
1 brd
2 breach
111 break
2 breakdown
9 breakfast
3 breast
2 breath
2 breathe
2 breeze
2 bretharold
2 brian
6 bridge
3 bright
6 brilliant
72 bring
4 britishairways
3 bro
2 broadway
2 brokenpromises
15 brother
4 brothers
3 browser
3 browsers
4 bruh
4 brush
7 brutal
23 bs
2 btr
2 bttr
5 btv
23 btw
2 btwn
7 buck
2 bucket
3 buddies
3 buddy
4 budget
12 buf
16 buffalo
8 bug
4 build
2 bulkhead
3 bull
8 bullshit
2 bully
4 bum
9 bummer
36 bump
2 bumper
8 bunch
2 burbank
2 burger
5 burn
2 burningman
20 bus
6 bush
2 busiest
118 business
2 businessfirst
3 bussey
49 busy
1232 but
3 butt
8 button
2 buttonthatcalled
98 buy
3 buyer
45 bwi
3 bwiairport
2 bwisjd
387 by
3 bye
7 ca
15 cab
39 cabin
6 cabo
2 cache
4 cae
2 caffeine
5 cake
2 cal
3 calendar
4 calgary
6 cali
11 california
774 call
20 callback
2 callbacks
3 caller
3 callers
7 camera
2 camp
2 campilley
1755 can
4 cana
9 canada
6 canadian
1044 cancel
3 cancer
12 cancun
2 canx
2 cap
3 capaaviation
2 capable
3 capacity
3 capital
6 capt
24 captain
3 captive
3 capture
68 car
89 card
2 cardholder
166 care
2 career
3 cargo
2 caribbean
2 carol
3 carolina
15 carousel
3 carpet
2 carrie
11 carrier
10 carriers
13 carrieunderwood
53 carry
17 carryon
3 carryons
5 carseat
2 carsl
8 cart
46 case
8 cash
2 casimir
4 cat
35 catch
24 cater
2 cattle
42 cause
3 cb
22 cc
2 cdg
2 cebu
8 celebrate
12 cell
3 cellphone
26 center
9 central
2 centre
1 centric
2 centricity
1 cents
5 century
1 centuryca
49 ceo
3 cert
12 certain
11 certainly
14 certificate
3 cessna
1 cesspool
1 cflanagian
2 cha
2 chain
12 chair
19 chairman
8 challenge
92 chance
462 change
9 channel
7 chaos
2 chaotic
2 chapter
2 char
1 charac
16 character
1 characterize
138 charge
6 charity
2 charles
7 charleston
65 charlotte
3 charm
2 chart
4 charter
6 chase
2 chasefoster
12 chat
18 cheap
13 cheaper
4 cheapflights
5 cheat
438 check
58 checkin
3 checkins
4 checkout
3 checkpoint
16 cheer
2 cheertymedad
4 cheese
2 cheesy
2 chef
4 cherry
3 chg
7 chi
82 chicago
3 chicagotribune
6 chicken
3 chief
29 child
2 childish
18 children
4 chill
2 chimid
5 china
2 chip
2 chk
2 chkd
2 chnge
3 cho
4 chocolate
27 choice
4 choices
32 choose
2 choppiest
3 christmas
4 chrome
2 chronicleherald
3 chs
2 cincinnati
3 cincy
6 circle
5 circumstances
2 cite
15 cities
3 citizen
45 city
116 claim
2 clarence
5 clarification
6 clarify
2 clarkey
127 class
5 classy
2 claudoakeshott
3 cld
2 cldnt
9 cle
26 clean
28 clear
19 clearly
2 clesfo
9 cleveland
4 clever
14 click
7 client
8 clients
3 cll
2 clockwork
56 close
2 closepwcs
6 closer
5 closest
39 clothe
4 cloud
3 clown
93 clt
50 club
16 clue
2 clueless
5 cluster
2 clutch
15 cmh
11 cmon
9 cnn
3 cnnbrk
2 cnnmoney
3 cnx
2 cnxn
3 co
20 coach
10 coast
14 coat
2 cockpit
3 cocktail
2 cod
29 code
3 codeshare
18 coffee
4 coincidence
3 coke
33 cold
3 colleague
3 colleagues
3 collect
4 collection
7 college
2 colo
2 colombia
6 color
3 columbia
17 columbus
3 combination
6 combine
4 comcast
228 come
3 comedy
5 comfort
8 comfortable
2 comfortably
3 comm
2 command
14 comment
11 commercial
6 commercials
4 commit
6 commitment
8 common
2 comms
13 communicate
41 communication
2 communicationfail
5 communications
3 community
9 comp
42 companion
90 company
7 compare
10 compassion
2 compatible
3 comped
31 compensate
36 compensation
3 compete
3 competent
4 competition
3 competitor
2 competitors
20 complain
45 complaint
26 complaints
35 complete
37 completely
2 compliant
3 complicate
7 compliment
9 complimentary
2 compound
2 comps
45 computer
14 computers
3 con
2 concentrate
3 concept
44 concern
4 concert
6 concourse
2 condescend
20 condition
2 condomrito
2 conection
27 conf
9 conference
3 confidence
4 confident
76 confirm
66 confirmation
2 confirmations
6 conflict
15 confuse
7 confusion
17 congrats
4 congratulations
130 connect
151 connection
36 connections
4 connector
4 consecutive
31 consider
2 considerable
5 consideration
2 consistency
9 consistent
8 consistently
2 consolation
6 constant
5 constantly
2 construction
3 consumers
9 cont
121 contact
3 contain
2 contd
7 content
12 contest
2 context
5 continental
3 contingency
2 continually
36 continue
4 continuous
6 contract
31 control
2 controllers
2 conveniently
5 conversation
3 conversations
2 conveyer
4 convince
3 convo
4 cookies
64 cool
3 coordinate
2 copilot
7 copy
2 corevalues
2 corner
5 corp
15 corporate
7 corporation
44 correct
8 correctly
2 cos
76 cost
4 costa
9 costumer
2 costumers
4 cot
2 cots
2 cough
352 could
74 count
45 counter
32 country
2 county
17 couple
7 coupon
4 coupons
2 courier
29 course
3 court
7 courteous
15 courtesy
6 cousin
30 cover
3 coverage
9 cowboycerrone
3 coworker
3 coworkers
3 cp
2 cpap
2 cr
5 crackers
2 craft
4 cramp
5 cranky
13 crap
14 crappy
19 crash
2 crate
2 crave
3 craziness
33 crazy
2 cream
14 create
2 creative
135 credit
245 crew
3 crewmember
3 crewmembers
2 criminal
5 crisis
2 crook
37 cross
5 crowd
3 cruel
4 cruise
15 cry
25 cs
8 csr
2 css
4 ct
7 ctl
2 ctr
6 cuba
4 cue
3 culture
11 cun
7 cup
3 curbside
2 curiosity
8 curious
22 current
44 currently
2 cus
4 cushion
54 cust
2 custexp
746 customer
2 customerexperience
3 customerrelations
183 customers
37 customerservice
5 customerservicefail
6 customs
5 custserv
29 cut
5 cute
9 cuz
6 cvg
2 cvgairport
7 cx
8 cxl
3 cxld
3 cycle
12 dad
2 dads
17 daily
2 dakota
16 dal
2 dalaus
2 dale
77 dallas
2 dallasaustin
5 dallaslovefield
2 dallasnews
25 damage
22 damn
2 danahajek
8 dance
5 dang
2 danger
3 dare
4 dark
5 darn
6 data
49 date
28 daughter
6 daughters
10 david
2 dawn
335 day
230 days
4 daytona
60 dc
102 dca
3 dcabos
2 dcoadavon
6 de
2 deactivate
13 dead
3 deadhead
3 deaf
103 deal
2 deane
7 dear
14 death
4 debacle
2 debate
2 debit
7 december
3 decency
7 decent
19 decide
10 decision
6 decisions
4 deck
3 decline
2 dedicate
2 deedee
7 deep
4 def
2 defend
9 define
22 definitely
4 definition
2 definitive
2 deflator
8 degree
9 degrees
21 deice
2 del
982 delay
3 delayedcancelled
3 delayscancelled
7 delete
4 delhi
2 delicious
4 delight
56 deliver
19 delivery
108 delta
4 deltaassist
2 demand
2 demo
47 den
2 denairport
80 denver
2 denverairport
24 deny
2 deodorant
4 dep
70 depart
19 department
92 departure
9 departures
4 depend
23 deplane
4 deplorable
18 dept
4 derekc
3 derrick
2 des
6 describe
2 desert
26 deserve
14 design
3 desire
68 desk
6 desks
5 desktop
7 desperately
2 despicable
33 despite
6 dest
62 destination
2 destinationdragon
83 destinationdragons
7 destinations
6 destroy
44 detail
2 determine
6 detroit
2 devalue
4 develop
2 developers
2 development
8 device
4 devices
146 dfw
17 dfwairport
14 dia
2 diabetic
7 dial
2 dialogue
2 dialup
2 diamond
2 diapers
2 dice
2 dick
20 die
19 diego
17 diff
21 difference
90 different
2 differently
12 difficult
2 difficulties
2 difficulty
3 dig
6 digital
2 dim
5 dime
3 din
2 ding
10 dinner
95 direct
2 direction
13 directly
6 directtv
10 dirty
2 disability
3 disable
13 disappear
126 disappoint
10 disappointment
15 disaster
55 disconnect
3 discontinue
18 discount
6 discover
3 discrimination
13 discuss
8 disgrace
3 disgraceful
3 disgruntle
25 disgust
2 disinfectant
3 dislike
7 disney
3 disorganization
4 disorganize
2 dispatch
2 dispatcher
7 display
2 displease
3 dispute
7 disregard
6 disrespect
4 disrespectful
2 disrupt
2 disruptions
2 dissapointed
2 dissatisfaction
6 dissatisfy
3 distance
2 distress
3 distribution
4 div
3 diversion
2 diversions
16 divert
29 dividend
2 dividendmiles
2 dividendrewards
4 dividends
4 dl
269 dm
2 dmd
18 dmed
6 dming
3 dms
2 dnt
2538 do
5 dobetter
3 doctor
6 document
15 dog
2 doke
7 dollar
16 dollars
25 domestic
2 dominican
2 donate
2 donatert
4 donation
2 donkey
2 doo
26 door
11 doors
5 dot
15 double
9 doubt
2 doubtful
4 doug
134 down
9 downgrade
3 downhill
7 download
3 downtown
5 dozen
4 dozens
4 dpt
5 dr
2 draft
7 dragon
18 dragons
2 dramatically
5 draw
2 dre
2 dread
13 dream
2 dreamliner
2 dress
47 drink
76 drive
6 driver
62 drop
4 drunks
5 dry
4 dsm
8 dtw
3 dublin
3 dude
240 due
3 duffle
4 duh
12 dull
4 dullesairport
2 dultch
8 dumb
5 dump
4 dunno
2 dupe
3 durango
59 during
4 duty
50 each
2 eagle
2 ear
93 earlier
2 earliest
97 early
3 earlybird
26 earn
6 earth
2 earthquake
12 easier
4 easiest
13 easily
13 east
8 eastern
36 easy
18 eat
2 eb
3 eco
3 econ
15 economy
2 ed
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3 westjet
12 wet
2 whammie
818 what
10 whatever
14 wheel
12 wheelchair
2 wheelchairs
3 wheelsup
643 when
10 whenever
185 where
9 whether
104 which
90 while
2 whiplash
2 whisper
7 white
226 who
3 whoa
7 whoever
43 whole
12 whose
2 wht
593 why
3 wichita
2 wide
86 wife
126 wifi
1228 will
42 win
8 wind
25 window
3 windows
7 wine
6 wing
2 winner
11 winners
34 winter
3 winterstorm
4 winterweather
4 wipe
2 wire
48 wish
1511 with
16 within
104 without
3 witness
2 wk
4 wknd
2 wkrb
5 wks
3 wld
3 wmcactionnews
3 wmy
2 wn
4 wno
3 woaw
26 wof
15 woman
7 women
40 wonder
29 wonderful
3 woo
3 woof
4 woohoo
44 word
407 work
2 workaround
4 worker
11 workers
49 world
54 worry
62 worse
240 worst
5 worstairline
8 worstairlineever
7 worstcustomerservice
3 worstcustomerserviceever
2 worstflight
3 worstflightever
3 worstunitedflightsever
29 worth
6 worthless
737 would
2 wouldbe
2 wound
3 wour
10 wout
45 wow
3 wrap
5 wreck
33 write
76 wrong
2 wrongfully
15 wsj
41 wtf
8 wth
2 wudda
2 wut
2 wx
10 wyou
3 xdaily
3 xday
3 xna
2 xoxo
2 xt
3 xweekly
7 xx
13 ya
2 yards
11 yay
12 yea
43 yeah
97 year
2 yearly
47 years
15 yell
17 yep
239 yes
4 yest
107 yesterday
154 yet
2 yikes
17 yo
2 yof
11 york
4796 you
2 youareonyourown
3 youk
9 young
2 younger
1735 your
7 yours
5 yourself
5 yourselves
8 yous
4 yousuck
3 youth
2 yow
36 yr
10 yrs
2 yuck
4 yuma
2 yummy
9 yup
2 yvonne
7 yvr
14 yyz
36 zero
3 zkatcher
10 zone
2 zoom
4 zurich

#TfidfVectorizer
tfidf_vectorizer_DF = TfidfVectorizer(analyzer = "word",tokenizer = None, preprocessor = None, stop_words = None,max_features = 5000, min_df=5, max_df=0.7,ngram_range=(1,2)) 
tfidf_vectorizer_features_DF = tfidf_vectorizer_DF.fit_transform(pre_Processing_Tweets)
tfidf_vectorizer_features_DF = tfidf_vectorizer_features_DF.toarray()

print (tfidf_vectorizer_features_DF.shape)
print(tfidf_vectorizer_features_DF)

(14568, 5000)
[[0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 ...
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]
 [0. 0. 0. ... 0. 0. 0.]]

#Vocabulary
tfidf_vectorizer_vocab_DF = tfidf_vectorizer_DF.get_feature_names()
print (tfidf_vectorizer_vocab_DF)

['aa', 'aa be', 'aa do', 'aa flight', 'aadvantage', 'able', 'able to', 'about', 'about an', 'about it', 'about my', 'about our', 'about the', 'about this', 'about to', 'about you', 'about your', 'above', 'abq', 'absolute', 'absolutely', 'absurd', 'ac', 'accept', 'acceptable', 'access', 'access to', 'accommodate', 'accord', 'accord to', 'account', 'acct', 'accurate', 'across', 'act', 'action', 'actual', 'actually', 'add', 'add my', 'add to', 'additional', 'address', 'admirals', 'admirals club', 'advance', 'advantage', 'advertise', 'advise', 'advisory', 'affect', 'afford', 'after', 'after an', 'after be', 'after hours', 'after land', 'after my', 'after the', 'after wait', 'afternoon', 'again', 'again for', 'again you', 'age', 'agent', 'agent at', 'agent be', 'agent in', 'agent on', 'agents', 'agents at', 'agents be', 'ago', 'ago and', 'ago be', 'agree', 'ah', 'ahead', 'air', 'aircraft', 'airfare', 'airline', 'airline be', 'airline ever', 'airline have', 'airline in', 'airline that', 'airline to', 'airlines', 'airlines be', 'airplane', 'airport', 'airport and', 'airport be', 'airport for', 'airport http', 'airport in', 'airport to', 'airports', 'airways', 'alert', 'alist', 'all', 'all be', 'all day', 'all flight', 'all get', 'all good', 'all have', 'all my', 'all night', 'all of', 'all set', 'all that', 'all the', 'allow', 'allow me', 'allow to', 'almost', 'almost an', 'almost hours', 'almost hrs', 'alone', 'along', 'already', 'already do', 'already have', 'also', 'alternate', 'although', 'always', 'always be', 'amaze', 'america', 'american', 'american airlines', 'american http', 'americanair', 'americanair aa', 'americanair and', 'americanair any', 'americanair be', 'americanair but', 'americanair can', 'americanair cancel', 'americanair do', 'americanair flight', 'americanair get', 'americanair great', 'americanair have', 'americanair hey', 'americanair hi', 'americanair how', 'americanair if', 'americanair it', 'americanair its', 'americanair just', 'americanair love', 'americanair my', 'americanair need', 'americanair no', 'americanair not', 'americanair now', 'americanair oh', 'americanair ok', 'americanair on', 'americanair please', 'americanair really', 'americanair she', 'americanair so', 'americanair still', 'americanair thank', 'americanair that', 'americanair the', 'americanair there', 'americanair they', 'americanair this', 'americanair thx', 'americanair to', 'americanair try', 'americanair understand', 'americanair unite', 'americanair usairways', 'americanair we', 'americanair well', 'americanair what', 'americanair when', 'americanair why', 'americanair will', 'americanair would', 'americanair yes', 'americanair you', 'americanair your', 'americanairlines', 'among', 'amount', 'amount of', 'an', 'an agent', 'an airline', 'an airport', 'an amaze', 'an answer', 'an apology', 'an automate', 'an earlier', 'an email', 'an entire', 'an error', 'an extra', 'an hour', 'an hr', 'an international', 'an issue', 'an option', 'an update', 'an upgrade', 'and', 'and after', 'and all', 'and be', 'and book', 'and call', 'and can', 'and cancel', 'and count', 'and delay', 'and do', 'and find', 'and flight', 'and fly', 'and for', 'and from', 'and get', 'and give', 'and go', 'and half', 'and hang', 'and have', 'and he', 'and help', 'and hours', 'and how', 'and in', 'and it', 'and just', 'and keep', 'and leave', 'and lose', 'and make', 'and minutes', 'and miss', 'and more', 'and my', 'and need', 'and never', 'and no', 'and not', 'and now', 'and on', 'and only', 'and our', 'and out', 'and pay', 'and put', 'and say', 'and service', 'and she', 'and still', 'and take', 'and tell', 'and thank', 'and that', 'and the', 'and their', 'and then', 'and there', 'and they', 'and this', 'and to', 'and try', 'and wait', 'and wall', 'and want', 'and we', 'and what', 'and when', 'and will', 'and would', 'and you', 'and your', 'angry', 'announce', 'announcement', 'annoy', 'another', 'another airline', 'another delay', 'another flight', 'another hour', 'answer', 'answer my', 'answer on', 'answer the', 'answer your', 'any', 'any chance', 'any flight', 'any help', 'any idea', 'any info', 'any other', 'any update', 'any way', 'anymore', 'anyone', 'anyone on', 'anything', 'anything about', 'anything to', 'anything you', 'anytime', 'anyway', 'anywhere', 'apart', 'apologies', 'apologize', 'apology', 'app', 'app be', 'app say', 'apparently', 'appear', 'appear to', 'appease', 'appease passengers', 'apple', 'apply', 'appreciate', 'appreciate it', 'appreciate that', 'appreciate the', 'appreciate you', 'appreciate your', 'approach', 'april', 'area', 'around', 'around the', 'arrival', 'arrive', 'arrive at', 'arrive in', 'as', 'as always', 'as an', 'as be', 'as have', 'as it', 'as possible', 'as soon', 'as the', 'as they', 'as we', 'as well', 'as you', 'ashamed', 'ask', 'ask for', 'ask if', 'ask me', 'ask to', 'ass', 'assign', 'assignment', 'assist', 'assistance', 'assume', 'at', 'at airport', 'at all', 'at baggage', 'at be', 'at bwi', 'at clt', 'at customer', 'at dca', 'at dfw', 'at ewr', 'at gate', 'at iad', 'at iah', 'at jfk', 'at lax', 'at least', 'at my', 'at newark', 'at ord', 'at phl', 'at pm', 'at sfo', 'at the', 'at this', 'at what', 'at your', 'atl', 'atlanta', 'attempt', 'attempt to', 'attend', 'attendant', 'attendants', 'attention', 'attitude', 'august', 'aus', 'austin', 'australia', 'auto', 'automate', 'automate system', 'automatically', 'avail', 'available', 'available on', 'avgeek', 'aviation', 'avoid', 'award', 'award travel', 'aware', 'away', 'away from', 'awesome', 'awesome flight', 'awesome thank', 'awful', 'ba', 'baby', 'back', 'back and', 'back at', 'back for', 'back from', 'back home', 'back in', 'back late', 'back on', 'back to', 'bad', 'bad experience', 'bad weather', 'badcustomerservice', 'badly', 'badservice', 'bag', 'bag and', 'bag at', 'bag be', 'bag come', 'bag do', 'bag for', 'bag from', 'bag have', 'bag in', 'bag on', 'bag that', 'bag to', 'baggage', 'baggage be', 'baggage claim', 'baggage fee', 'baggage office', 'baggage to', 'balance', 'ball', 'band', 'bank', 'base', 'base on', 'basically', 'bathroom', 'battle', 'battle to', 'bc', 'bc of', 'bday', 'bdl', 'be', 'be able', 'be about', 'be absolutely', 'be absurd', 'be after', 'be all', 'be allow', 'be almost', 'be already', 'be also', 'be always', 'be amaze', 'be an', 'be and', 'be answer', 'be any', 'be anyone', 'be appreciate', 'be as', 'be ashamed', 'be ask', 'be at', 'be available', 'be awesome', 'be awful', 'be back', 'be bad', 'be be', 'be better', 'be beyond', 'be board', 'be book', 'be break', 'be busy', 'be but', 'be call', 'be cancel', 'be change', 'be charge', 'be check', 'be close', 'be come', 'be complete', 'be completely', 'be crazy', 'be currently', 'be customer', 'be deal', 'be delay', 'be deliver', 'be deny', 'be do', 'be down', 'be due', 'be empty', 'be expect', 'be finally', 'be find', 'be fine', 'be fix', 'be flight', 'be fly', 'be for', 'be force', 'be from', 'be frustrate', 'be full', 'be get', 'be give', 'be glad', 'be go', 'be good', 'be great', 'be hang', 'be happen', 'be happy', 'be have', 'be head', 'be help', 'be helpful', 'be here', 'be hold', 'be home', 'be honest', 'be hop', 'be horrible', 'be hours', 'be how', 'be hrs', 'be huge', 'be in', 'be it', 'be joke', 'be just', 'be kill', 'be late', 'be leave', 'be less', 'be lie', 'be like', 'be little', 'be load', 'be long', 'be look', 'be lose', 'be lot', 'be loyal', 'be make', 'be min', 'be minutes', 'be miss', 'be more', 'be move', 'be much', 'be my', 'be need', 'be never', 'be new', 'be next', 'be nice', 'be no', 'be not', 'be nothing', 'be now', 'be offer', 'be ok', 'be on', 'be one', 'be only', 'be open', 'be our', 'be out', 'be over', 'be patient', 'be pay', 'be possible', 'be pretty', 'be probably', 'be problem', 'be put', 'be ready', 'be really', 'be rebooked', 'be refund', 'be return', 'be ridiculous', 'be right', 'be rude', 'be run', 'be say', 'be schedule', 'be seat', 'be send', 'be sit', 'be so', 'be sorry', 'be stick', 'be still', 'be strand', 'be such', 'be super', 'be suppose', 'be sure', 'be take', 'be talk', 'be tell', 'be terrible', 'be that', 'be the', 'be there', 'be they', 'be this', 'be to', 'be today', 'be too', 'be totally', 'be travel', 'be treat', 'be try', 'be tweet', 'be ua', 'be unable', 'be unacceptable', 'be up', 'be use', 'be very', 'be wait', 'be we', 'be what', 'be where', 'be why', 'be will', 'be with', 'be wonderful', 'be work', 'be worse', 'be wrong', 'be you', 'be your', 'beach', 'beat', 'beautiful', 'because', 'because be', 'because it', 'because my', 'because of', 'because the', 'because they', 'because we', 'because you', 'because your', 'become', 'before', 'before board', 'before flight', 'before the', 'begin', 'behind', 'believe', 'believe it', 'believe you', 'belong', 'benefit', 'best', 'best airline', 'best flight', 'bet', 'better', 'better than', 'between', 'beyond', 'bf', 'big', 'big thank', 'biggest', 'bill', 'bin', 'bird', 'birthday', 'bite', 'biz', 'black', 'blame', 'blog', 'blow', 'blue', 'bna', 'board', 'board and', 'board flight', 'board pass', 'board the', 'bonus', 'book', 'book flight', 'book for', 'book my', 'book on', 'book online', 'book problems', 'book the', 'book ticket', 'book with', 'bos', 'bos to', 'boston', 'both', 'bother', 'brand', 'break', 'breakfast', 'bring', 'brother', 'bs', 'btw', 'buf', 'buffalo', 'bummer', 'bump', 'bus', 'business', 'business class', 'busy', 'but', 'but be', 'but can', 'but do', 'but get', 'but have', 'but if', 'but it', 'but my', 'but need', 'but no', 'but not', 'but now', 'but still', 'but thank', 'but that', 'but the', 'but then', 'but there', 'but they', 'but this', 'but we', 'but when', 'but why', 'but will', 'but you', 'but your', 'buy', 'bwi', 'by', 'by phone', 'by pm', 'by the', 'by your', 'cab', 'cabin', 'california', 'call', 'call and', 'call back', 'call be', 'call center', 'call customer', 'call for', 'call from', 'call in', 'call it', 'call me', 'call the', 'call to', 'call volume', 'call you', 'call your', 'callback', 'can', 'can be', 'can book', 'can call', 'can dm', 'can do', 'can fly', 'can get', 'can help', 'can make', 'can not', 'can see', 'can send', 'can someone', 'can tell', 'can we', 'can you', 'cancel', 'cancel flight', 'cancel flightation', 'cancel flightations', 'cancel flightlation', 'cancel flightlations', 'cancel flightled', 'cancel flightling', 'cancun', 'captain', 'car', 'car seat', 'card', 'care', 'care about', 'care of', 'carousel', 'carrier', 'carriers', 'carrieunderwood', 'carry', 'carry on', 'carryon', 'case', 'catch', 'cater', 'cause', 'cc', 'cell', 'center', 'ceo', 'ceo battle', 'certain', 'certainly', 'certificate', 'chair', 'chairman', 'chance', 'chance of', 'chance to', 'chance you', 'change', 'change fee', 'change flight', 'change in', 'change it', 'change my', 'change the', 'change ticket', 'change to', 'character', 'charge', 'charge for', 'charge me', 'charlotte', 'chat', 'cheap', 'cheaper', 'check', 'check bag', 'check baggage', 'check in', 'check my', 'check the', 'checkin', 'cheer', 'chicago', 'child', 'children', 'choice', 'choose', 'cities', 'city', 'claim', 'class', 'class seat', 'clean', 'clear', 'clearly', 'click', 'close', 'clothe', 'clt', 'clt to', 'club', 'clue', 'cmh', 'cmon', 'coach', 'coast', 'coat', 'code', 'coffee', 'cold', 'columbus', 'come', 'come back', 'come from', 'come in', 'come on', 'come out', 'come to', 'come up', 'comment', 'commercial', 'communicate', 'communication', 'companion', 'companion pass', 'company', 'compassion', 'compensate', 'compensate for', 'compensation', 'complain', 'complaint', 'complaints', 'complete', 'completely', 'computer', 'computers', 'concern', 'condition', 'conf', 'confirm', 'confirmation', 'confirmation number', 'confuse', 'congrats', 'connect', 'connect flight', 'connect to', 'connection', 'connection to', 'connections', 'consider', 'contact', 'contact me', 'contact you', 'contest', 'continue', 'continue to', 'control', 'cool', 'corporate', 'correct', 'cost', 'cost me', 'cost of', 'could', 'could be', 'could get', 'could have', 'could not', 'could you', 'count', 'counter', 'country', 'couple', 'course', 'courtesy', 'cover', 'crap', 'crappy', 'crash', 'crazy', 'create', 'credit', 'credit card', 'credit for', 'crew', 'crew be', 'crew have', 'crew on', 'cross', 'cross country', 'cry', 'cs', 'cun', 'current', 'currently', 'cust', 'cust service', 'cust svc', 'customer', 'customer care', 'customer experience', 'customer relations', 'customer service', 'customers', 'customers be', 'customerservice', 'cut', 'dad', 'daily', 'dal', 'dallas', 'dallas to', 'damage', 'damn', 'date', 'daughter', 'david', 'day', 'day and', 'day of', 'day to', 'days', 'days ago', 'days and', 'days late', 'days to', 'dc', 'dca', 'dca to', 'dead', 'deal', 'deal with', 'death', 'decide', 'decide to', 'decision', 'definitely', 'deice', 'delay', 'delay again', 'delay and', 'delay be', 'delay because', 'delay but', 'delay by', 'delay due', 'delay flight', 'delay for', 'delay from', 'delay hours', 'delay hrs', 'delay in', 'delay my', 'delay on', 'delay or', 'delay over', 'delay so', 'delay to', 'deliver', 'delivery', 'delta', 'den', 'den to', 'denver', 'deny', 'depart', 'department', 'departure', 'departure time', 'deplane', 'dept', 'deserve', 'design', 'desk', 'despite', 'destination', 'destinationdragons', 'detail', 'dfw', 'dfw be', 'dfw to', 'dfwairport', 'dia', 'die', 'diego', 'diff', 'difference', 'different', 'different flight', 'difficult', 'dinner', 'direct', 'direct flight', 'directly', 'dirty', 'disappear', 'disappoint', 'disappoint in', 'disappoint with', 'disappointment', 'disaster', 'disconnect', 'discount', 'discuss', 'disgust', 'divert', 'dividend', 'dividend miles', 'dm', 'dm me', 'dm send', 'dm you', 'dmed', 'dmed you', 'do', 'do and', 'do anyone', 'do anything', 'do be', 'do better', 'do but', 'do do', 'do for', 'do get', 'do great', 'do have', 'do it', 'do my', 'do need', 'do not', 'do nothing', 'do so', 'do something', 'do thank', 'do that', 'do the', 'do they', 'do this', 'do to', 'do we', 'do with', 'do you', 'do your', 'dog', 'dollars', 'domestic', 'door', 'doors', 'double', 'down', 'down the', 'downgrade', 'dragons', 'dream', 'drink', 'drive', 'drive to', 'drop', 'drop the', 'due', 'due to', 'dull', 'during', 'during the', 'each', 'each other', 'each time', 'earlier', 'earlier flight', 'early', 'early bird', 'earn', 'easier', 'easily', 'east', 'easy', 'eat', 'economy', 'effort', 'either', 'elevate', 'elite', 'else', 'elsewhere', 'email', 'email address', 'email be', 'email that', 'email to', 'embarrass', 'emergency', 'employee', 'employees', 'employees be', 'empty', 'empty seat', 'end', 'end of', 'end up', 'engine', 'enjoy', 'enough', 'enough to', 'enter', 'entertainment', 'entire', 'epicfail', 'equipment', 'error', 'errors', 'especially', 'est', 'estimate', 'etc', 'even', 'even get', 'even let', 'even more', 'even though', 'event', 'eventually', 'ever', 'ever experience', 'ever fly', 'ever have', 'ever see', 'every', 'every single', 'every time', 'everyone', 'everyone be', 'everything', 'everything be', 'ewr', 'ewr to', 'exact', 'exactly', 'example', 'excellent', 'except', 'excite', 'excite to', 'excuse', 'excuse for', 'exec', 'exist', 'exit', 'exit row', 'expect', 'expect to', 'expensive', 'experience', 'experience be', 'experience ever', 'experience have', 'experience with', 'expire', 'explain', 'explanation', 'express', 'extend', 'extra', 'extremely', 'eye', 'fa', 'faa', 'face', 'fact', 'fact that', 'fail', 'fail to', 'failure', 'fair', 'fall', 'false', 'fam', 'family', 'fan', 'fantastic', 'far', 'fare', 'fast', 'faster', 'father', 'fault', 'favorite', 'fb', 'feature', 'feb', 'february', 'fee', 'fee for', 'feedback', 'feel', 'feel like', 'feet', 'felt', 'few', 'few days', 'few weeks', 'field', 'fight', 'figure', 'figure it', 'figure out', 'file', 'file claim', 'fill', 'fill out', 'final', 'final destination', 'finally', 'finally get', 'find', 'find it', 'find my', 'find out', 'find the', 'fine', 'finger', 'finger cross', 'finish', 'fire', 'first', 'first class', 'first flight', 'first time', 'fit', 'five', 'fix', 'fix it', 'fix the', 'fix this', 'fix your', 'fl', 'fleek', 'fleek http', 'fleet', 'fleet on', 'flight', 'flight aa', 'flight after', 'flight again', 'flight all', 'flight and', 'flight arrive', 'flight as', 'flight at', 'flight attendant', 'flight attendants', 'flight back', 'flight bc', 'flight be', 'flight because', 'flight book', 'flight but', 'flight by', 'flight can', 'flight cancel', 'flight change', 'flight crew', 'flight delay', 'flight do', 'flight due', 'flight even', 'flight experience', 'flight flight', 'flight for', 'flight from', 'flight get', 'flight go', 'flight have', 'flight help', 'flight home', 'flight how', 'flight http', 'flight if', 'flight in', 'flight info', 'flight into', 'flight it', 'flight just', 'flight land', 'flight last', 'flight leave', 'flight my', 'flight no', 'flight not', 'flight now', 'flight number', 'flight of', 'flight on', 'flight online', 'flight or', 'flight our', 'flight out', 'flight plan', 'flight please', 'flight schedule', 'flight so', 'flight status', 'flight still', 'flight thank', 'flight that', 'flight the', 'flight then', 'flight they', 'flight this', 'flight time', 'flight to', 'flight today', 'flight tomorrow', 'flight tonight', 'flight too', 'flight ua', 'flight us', 'flight want', 'flight we', 'flight what', 'flight when', 'flight will', 'flight with', 'flight without', 'flight would', 'flight yesterday', 'flight you', 'flight your', 'flightation', 'flightations', 'flightd', 'flightlation', 'flightlations', 'flightled', 'flightled and', 'flightled be', 'flightled can', 'flightled flight', 'flightled for', 'flightled my', 'flightled our', 'flightled the', 'flightling', 'flightly', 'flightr', 'flightr and', 'flightr still', 'fll', 'fll to', 'floor', 'florida', 'flt', 'flt be', 'flt from', 'flt to', 'flts', 'fly', 'fly back', 'fly experience', 'fly from', 'fly in', 'fly into', 'fly jetblue', 'fly on', 'fly out', 'fly southwest', 'fly to', 'fly unite', 'fly us', 'fly usairways', 'fly with', 'fly you', 'fly your', 'flyer', 'flyers', 'flyfi', 'flyingitforward', 'flytpa', 'folks', 'follow', 'follow me', 'follow up', 'follow you', 'food', 'for', 'for about', 'for all', 'for almost', 'for an', 'for another', 'for assistance', 'for bag', 'for baggage', 'for be', 'for call', 'for cancel', 'for check', 'for customer', 'for days', 'for delay', 'for dm', 'for flight', 'for free', 'for gate', 'for get', 'for help', 'for her', 'for hotel', 'for hour', 'for hours', 'for hr', 'for hrs', 'for it', 'for lose', 'for luggage', 'for make', 'for me', 'for min', 'for mins', 'for minutes', 'for more', 'for my', 'for new', 'for next', 'for no', 'for not', 'for nothing', 'for one', 'for our', 'for over', 'for people', 'for reflight', 'for refund', 'for reply', 'for respond', 'for response', 'for seat', 'for send', 'for some', 'for someone', 'for take', 'for 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'sky', 'slc', 'sleep', 'sleep in', 'slow', 'small', 'smh', 'smile', 'smooth', 'sna', 'snack', 'snow', 'snow in', 'so', 'so be', 'so can', 'so do', 'so far', 'so frustrate', 'so have', 'so it', 'so late', 'so long', 'so many', 'so much', 'so no', 'so that', 'so the', 'so they', 'so we', 'so what', 'so why', 'so will', 'so you', 'social', 'social media', 'solution', 'solve', 'some', 'some help', 'some of', 'somebody', 'somehow', 'someone', 'someone about', 'someone be', 'someone else', 'someone on', 'someone please', 'someone to', 'something', 'somewhere', 'son', 'song', 'soon', 'soon as', 'sooner', 'sorry', 'sorry for', 'sorry to', 'sort', 'sort of', 'sort out', 'sound', 'sound like', 'south', 'southwest', 'southwestair', 'southwestair and', 'southwestair any', 'southwestair be', 'southwestair can', 'southwestair cancel', 'southwestair could', 'southwestair do', 'southwestair finally', 'southwestair flight', 'southwestair fly', 'southwestair fortunemagazine', 'southwestair get', 'southwestair great', 'southwestair have', 'southwestair hey', 'southwestair hi', 'southwestair how', 'southwestair if', 'southwestair it', 'southwestair just', 'southwestair look', 'southwestair love', 'southwestair lovedragonss', 'southwestair my', 'southwestair need', 'southwestair no', 'southwestair not', 'southwestair now', 'southwestair oh', 'southwestair on', 'southwestair please', 'southwestair really', 'southwestair send', 'southwestair so', 'southwestair thank', 'southwestair that', 'southwestair the', 'southwestair this', 'southwestair thx', 'southwestair try', 'southwestair we', 'southwestair what', 'southwestair when', 'southwestair why', 'southwestair will', 'southwestair would', 'southwestair yes', 'southwestair you', 'southwestair your', 'space', 'speak', 'speak to', 'speak with', 'special', 'specific', 'speed', 'spend', 'split', 'spot', 'spring', 'st', 'st class', 'staff', 'staff at', 'staff be', 'stand', 'stand in', 'standby', 'standby for', 'star', 'start', 'start to', 'state', 'status', 'status match', 'status of', 'stay', 'stay in', 'stay on', 'steal', 'step', 'stewardess', 'stick', 'stick at', 'stick in', 'stick on', 'stick with', 'still', 'still be', 'still can', 'still do', 'still get', 'still have', 'still in', 'still no', 'still not', 'still on', 'still say', 'still sit', 'still wait', 'stl', 'stop', 'storm', 'story', 'straight', 'strand', 'strand in', 'street', 'stress', 'stuff', 'submit', 'such', 'suck', 'suggest', 'suggestions', 'suit', 'suitcase', 'summer', 'sun', 'sunday', 'sunny', 'super', 'supervisor', 'support', 'suppose', 'suppose to', 'sure', 'sure it', 'sure what', 'sure why', 'sure you', 'surprise', 'svc', 'sw', 'swa', 'sweet', 'switch', 'switch to', 'system', 'system be', 'systems', 'tag', 'take', 'take care', 'take me', 'take my', 'take off', 'take the', 'take to', 'takeoff', 'talk', 'talk to', 'tampa', 'tarmac', 'tarmac for', 'tarmac in', 'taxi', 'tea', 'team', 'team be', 'tech', 'tell', 'tell by', 'tell it', 'tell me', 'tell my', 'tell that', 'tell the', 'tell them', 'tell to', 'tell us', 'tell you', 'term', 'terminal', 'terrible', 'terrible customer', 'terrible service', 'texas', 'text', 'th', 'than', 'than hours', 'than the', 'thank', 'thank again', 'thank but', 'thank for', 'thank so', 'thank to', 'thank will', 'thank you', 'thankyou', 'that', 'that and', 'that be', 'that but', 'that can', 'that do', 'that flight', 'that for', 'that get', 'that have', 'that help', 'that it', 'that just', 'that make', 'that mean', 'that my', 'that no', 'that plane', 'that the', 'that there', 'that they', 'that we', 'that will', 'that would', 'that you', 'that your', 'the', 'the aa', 'the agent', 'the agents', 'the air', 'the airline', 'the airport', 'the app', 'the back', 'the bag', 'the baggage', 'the be', 'the best', 'the call', 'the cancel', 'the captain', 'the case', 'the change', 'the cost', 'the counter', 'the country', 'the credit', 'the crew', 'the customer', 'the day', 'the delay', 'the detail', 'the door', 'the email', 'the end', 'the entire', 'the experience', 'the extra', 'the fact', 'the first', 'the flight', 'the follow', 'the form', 'the free', 'the future', 'the gate', 'the good', 'the great', 'the grind', 'the help', 'the hotel', 'the hour', 'the info', 'the issue', 'the lack', 'the last', 'the line', 'the link', 'the love', 'the money', 'the morning', 'the most', 'the new', 'the next', 'the night', 'the number', 'the one', 'the only', 'the oscars', 'the other', 'the passengers', 'the past', 'the people', 'the person', 'the phone', 'the pilot', 'the plane', 'the pm', 'the point', 'the price', 'the problem', 'the quick', 'the reason', 'the record', 'the refund', 'the reply', 'the response', 'the right', 'the runway', 'the same', 'the seat', 'the second', 'the service', 'the site', 'the situation', 'the snow', 'the st', 'the staff', 'the status', 'the system', 'the tarmac', 'the terminal', 'the th', 'the ticket', 'the time', 'the trip', 'the unite', 'the update', 'the upgrade', 'the us', 'the vegas', 'the voucher', 'the wait', 'the way', 'the weather', 'the website', 'the whole', 'the world', 'the worst', 'the wrong', 'their', 'their customers', 'their flight', 'them', 'them to', 'then', 'then cancel', 'then get', 'then it', 'then you', 'there', 'there any', 'there anything', 'there be', 'there have', 'there way', 'these', 'they', 'they be', 'they can', 'they cancel', 'they could', 'they do', 'they have', 'they just', 'they make', 'they say', 'they tell', 'they will', 'they would', 'thing', 'things', 'think', 'think about', 'think be', 'think it', 'think you', 'third', 'this', 'this afternoon', 'this airline', 'this be', 'this delay', 'this even', 'this flight', 'this happen', 'this have', 'this issue', 'this morning', 'this one', 'this plane', 'this point', 'this time', 'this to', 'this trip', 'this week', 'this weekend', 'this year', 'thnx', 'tho', 'those', 'though', 'three', 'three hours', 'through', 'through on', 'through the', 'through to', 'throw', 'thru', 'thursday', 'thx', 'thx for', 'ticket', 'ticket and', 'ticket be', 'ticket counter', 'ticket for', 'ticket from', 'ticket on', 'ticket to', 'ticket with', 'til', 'till', 'time', 'time and', 'time be', 'time but', 'time do', 'time fly', 'time for', 'time have', 'time in', 'time it', 'time of', 'time on', 'time or', 'time the', 'time this', 'time to', 'time today', 'time we', 'time with', 'time you', 'timely', 'tire', 'tire of', 'tix', 'tmrw', 'to', 'to aa', 'to add', 'to all', 'to an', 'to and', 'to another', 'to answer', 'to anyone', 'to appease', 'to arrive', 'to ask', 'to atl', 'to austin', 'to be', 'to board', 'to book', 'to bos', 'to boston', 'to bring', 'to buy', 'to call', 'to cancel', 'to catch', 'to change', 'to charge', 'to charlotte', 'to check', 'to chicago', 'to clt', 'to come', 'to compensate', 'to contact', 'to customer', 'to dallas', 'to dc', 'to dca', 'to deal', 'to delay', 'to delta', 'to den', 'to denver', 'to depart', 'to dfw', 'to different', 'to discuss', 'to dm', 'to do', 'to drive', 'to enter', 'to ewr', 'to figure', 'to find', 'to first', 'to fix', 'to flight', 'to fll', 'to fly', 'to follow', 'to gate', 'to get', 'to give', 'to go', 'to handle', 'to hang', 'to have', 'to hawaii', 'to hear', 'to help', 'to hold', 'to houston', 'to human', 'to iad', 'to iah', 'to it', 'to jfk', 'to just', 'to keep', 'to know', 'to la', 'to las', 'to late', 'to lax', 'to leave', 'to let', 'to lga', 'to load', 'to look', 'to lose', 'to love', 'to make', 'to mco', 'to me', 'to mechanical', 'to mention', 'to miss', 'to move', 'to my', 'to nashville', 'to new', 'to newark', 'to no', 'to not', 'to ny', 'to nyc', 'to offer', 'to ord', 'to orlando', 'to our', 'to pay', 'to pdx', 'to person', 'to philly', 'to phl', 'to phx', 'to pick', 'to purchase', 'to put', 'to rdu', 'to reach', 'to rebook', 'to receive', 'to redeem', 'to reschedule', 'to resolve', 'to respond', 'to run', 'to san', 'to say', 'to see', 'to send', 'to sfo', 'to share', 'to sit', 'to sleep', 'to someone', 'to speak', 'to spend', 'to st', 'to start', 'to stay', 'to switch', 'to take', 'to talk', 'to tell', 'to the', 'to them', 'to this', 'to travel', 'to treat', 'to try', 'to unite', 'to upgrade', 'to us', 'to use', 'to vegas', 'to visit', 'to wait', 'to watch', 'to weather', 'to win', 'to work', 'to you', 'to your', 'today', 'today and', 'today be', 'together', 'tomorrow', 'tomorrow and', 'tomorrow be', 'tomorrow morning', 'tonight', 'too', 'too bad', 'too busy', 'too late', 'too long', 'too many', 'too much', 'top', 'top of', 'total', 'totally', 'touch', 'tough', 'tpa', 'track', 'traffic', 'train', 'train your', 'transfer', 'travel', 'travel advisory', 'travel experience', 'travel for', 'travel from', 'travel to', 'travel with', 'traveler', 'travelers', 'treat', 'treat your', 'treatment', 'trip', 'trip be', 'trip to', 'trouble', 'true', 'trueblue', 'truly', 'trust', 'try', 'try again', 'try and', 'try call', 'try for', 'try it', 'try that', 'try to', 'tsa', 'tsa precheck', 'tuesday', 'turn', 'tv', 'tweet', 'twice', 'twitter', 'two', 'two days', 'two hours', 'two of', 'two weeks', 'tx', 'ty', 'type', 'ua', 'ua be', 'ua from', 'ual', 'ugh', 'uk', 'unable', 'unable to', 'unacceptable', 'unavailable', 'unbelievable', 'uncomfortable', 'under', 'under the', 'understand', 'understand but', 'understand that', 'understand the', 'understand why', 'unfortunately', 'unfriendlyskies', 'unhappy', 'unhelpful', 'unite', 'unite after', 'unite again', 'unite airlines', 'unite all', 'unite already', 'unite also', 'unite and', 'unite another', 'unite any', 'unite appreciate', 'unite as', 'unite be', 'unite book', 'unite but', 'unite can', 'unite cancel', 'unite club', 'unite customer', 'unite delay', 'unite dm', 'unite do', 'unite flight', 'unite flt', 'unite fly', 'unite for', 'unite gate', 'unite get', 'unite great', 'unite have', 'unite hey', 'unite hi', 'unite how', 'unite if', 'unite in', 'unite it', 'unite its', 'unite just', 'unite look', 'unite lose', 'unite make', 'unite miss', 'unite my', 'unite need', 'unite never', 'unite no', 'unite nope', 'unite not', 'unite now', 'unite of', 'unite ok', 'unite on', 'unite once', 'unite or', 'unite please', 'unite really', 'unite send', 'unite seriously', 'unite she', 'unite should', 'unite so', 'unite stick', 'unite still', 'unite take', 'unite thank', 'unite that', 'unite the', 'unite there', 'unite they', 'unite think', 'unite this', 'unite to', 'unite try', 'unite ua', 'unite understand', 'unite very', 'unite want', 'unite we', 'unite well', 'unite what', 'unite when', 'unite where', 'unite why', 'unite will', 'unite worst', 'unite would', 'unite yes', 'unite you', 'unite your', 'unitedairlines', 'unitedsucks', 'unless', 'unprofessional', 'unreal', 'until', 'until tomorrow', 'up', 'up after', 'up and', 'up at', 'up for', 'up in', 'up my', 'up on', 'up the', 'up to', 'up with', 'upcoming', 'update', 'update on', 'update your', 'upgrade', 'upgrade to', 'upset', 'ur', 'us', 'us air', 'us airways', 'us all', 'us and', 'us be', 'us for', 'us go', 'us home', 'us in', 'us know', 'us off', 'us on', 'us out', 'us that', 'us the', 'us to', 'us we', 'usa', 'usair', 'usairways', 'usairways after', 'usairways already', 'usairways americanair', 'usairways and', 'usairways any', 'usairways be', 'usairways but', 'usairways call', 'usairways can', 'usairways cancel', 'usairways delay', 'usairways do', 'usairways flight', 'usairways get', 'usairways good', 'usairways great', 'usairways have', 'usairways hey', 'usairways hi', 'usairways hours', 'usairways how', 'usairways if', 'usairways it', 'usairways just', 'usairways look', 'usairways make', 'usairways minutes', 'usairways my', 'usairways need', 'usairways never', 'usairways no', 'usairways not', 'usairways now', 'usairways ok', 'usairways on', 'usairways over', 'usairways please', 'usairways really', 'usairways seriously', 'usairways sit', 'usairways so', 'usairways stick', 'usairways still', 'usairways tell', 'usairways thank', 'usairways that', 'usairways the', 'usairways there', 'usairways they', 'usairways this', 'usairways try', 'usairways us', 'usairways we', 'usairways well', 'usairways what', 'usairways when', 'usairways why', 'usairways will', 'usairways worst', 'usairways would', 'usairways yes', 'usairways you', 'usairways your', 'usairwaysfail', 'use', 'use credit', 'use it', 'use my', 'use the', 'use to', 'use you', 'use your', 'useless', 'usual', 'usually', 'va', 'vacation', 'valid', 'value', 'vegas', 'vegas to', 'very', 'very disappoint', 'very frustrate', 'very helpful', 'very much', 'via', 'via dm', 'via phone', 'via twitter', 'video', 'view', 'virgin', 'virginamerica', 'virginamerica be', 'virginamerica can', 'virginamerica do', 'virginamerica have', 'virginamerica ladygaga', 'virginamerica love', 'virginamerica thank', 'virginamerica what', 'virginamerica why', 'virginamerica you', 'virginamerica your', 'virginatlantic', 'visit', 'voice', 'volume', 'volunteer', 'voucher', 'voucher for', 'vouchers', 'vx', 'wait', 'wait an', 'wait and', 'wait at', 'wait for', 'wait hours', 'wait in', 'wait min', 'wait mins', 'wait minutes', 'wait on', 'wait over', 'wait time', 'wait to', 'waive', 'wake', 'walk', 'wall', 'wall street', 'want', 'want me', 'want my', 'want to', 'warm', 'warn', 'washington', 'waste', 'watch', 'watch the', 'water', 'waterbury', 'waterbury republican', 'way', 'way to', 'way you', 'ways', 'we', 'we all', 'we be', 'we can', 'we could', 'we do', 'we get', 'we go', 'we have', 'we just', 'we land', 'we miss', 'we need', 'we should', 'we try', 'we wait', 'we want', 'we will', 'we would', 'weather', 'weather and', 'weather be', 'weather but', 'weather delay', 'weather in', 'weather relate', 'web', 'website', 'website and', 'website be', 'website say', 'website will', 'wed', 'wednesday', 'week', 'weekend', 'weeks', 'weeks ago', 'weight', 'welcome', 'well', 'well be', 'well do', 'west', 'wet', 'what', 'what about', 'what be', 'what can', 'what do', 'what give', 'what happen', 'what the', 'what they', 'what time', 'what to', 'what will', 'what you', 'whatever', 'wheel', 'wheelchair', 'when', 'when arrive', 'when ask', 'when be', 'when book', 'when call', 'when can', 'when check', 'when do', 'when flight', 'when fly', 'when get', 'when it', 'when land', 'when my', 'when the', 'when there', 'when they', 'when try', 'when we', 'when will', 'when you', 'whenever', 'where', 'where be', 'where do', 'where it', 'where my', 'where the', 'whether', 'which', 'which be', 'while', 'while be', 'who', 'who be', 'who can', 'who do', 'who have', 'whole', 'whose', 'why', 'why be', 'why can', 'why do', 'why have', 'why not', 'why the', 'why we', 'why will', 'why would', 'why you', 'wife', 'wife and', 'wifi', 'wifi be', 'wifi on', 'will', 'will be', 'will call', 'will do', 'will fly', 'will get', 'will give', 'will have', 'will it', 'will just', 'will keep', 'will make', 'will miss', 'will never', 'will not', 'will see', 'will send', 'will take', 'will thank', 'will the', 'will to', 'will try', 'will you', 'win', 'window', 'winners', 'winter', 'wish', 'wish you', 'with', 'with aa', 'with all', 'with an', 'with another', 'with customer', 'with delta', 'with flight', 'with kid', 'with me', 'with my', 'with new', 'with no', 'with our', 'with someone', 'with that', 'with the', 'with them', 'with this', 'with unite', 'with us', 'with you', 'with your', 'within', 'without', 'wof', 'woman', 'wonder', 'wonder if', 'wonderful', 'word', 'work', 'work and', 'work at', 'work for', 'work on', 'work out', 'work with', 'workers', 'world', 'worry', 'worse', 'worst', 'worst airline', 'worst customer', 'worst experience', 'worst service', 'worth', 'would', 'would appreciate', 'would be', 'would fly', 'would go', 'would have', 'would help', 'would it', 'would like', 'would love', 'would make', 'would not', 'would rather', 'would think', 'would you', 'wout', 'wow', 'write', 'wrong', 'wrong with', 'wsj', 'wtf', 'wyou', 'ya', 'yay', 'yea', 'yeah', 'year', 'year old', 'years', 'yell', 'yell at', 'yep', 'yes', 'yes but', 'yes do', 'yes it', 'yes please', 'yes thank', 'yesterday', 'yesterday and', 'yesterday be', 'yet', 'yet to', 'yo', 'york', 'you', 'you again', 'you all', 'you and', 'you ask', 'you back', 'you be', 'you but', 'you call', 'you can', 'you cancel', 'you change', 'you charge', 'you check', 'you could', 'you delay', 'you dm', 'you do', 'you expect', 'you fail', 'you find', 'you fly', 'you follow', 'you for', 'you get', 'you give', 'you go', 'you guy', 'you have', 'you help', 'you http', 'you if', 'you in', 'you just', 'you keep', 'you kid', 'you know', 'you let', 'you like', 'you look', 'you lose', 'you make', 'you mean', 'you might', 'you my', 'you need', 'you never', 'you not', 'you now', 'you offer', 'you on', 'you pay', 'you please', 'you provide', 'you put', 'you really', 'you say', 'you send', 'you should', 'you so', 'you still', 'you suck', 'you take', 'you tell', 'you thank', 'you the', 'you think', 'you to', 'you treat', 'you use', 'you very', 'you want', 'you we', 'you will', 'you would', 'you you', 'young', 'your', 'your agents', 'your airline', 'your app', 'your baggage', 'your call', 'your company', 'your crew', 'your customer', 'your customers', 'your employees', 'your fault', 'your flight', 'your gate', 'your help', 'your new', 'your own', 'your passengers', 'your people', 'your phone', 'your plan', 'your plane', 'your policy', 'your reservation', 'your reservations', 'your response', 'your service', 'your site', 'your staff', 'your system', 'your team', 'your website', 'your wifi', 'yr', 'yr old', 'yrs', 'yup', 'yyz', 'zero', 'zone']

#Stop Words
tfidf_vectorizer_stop_words = tfidf_vectorizer_DF.get_stop_words()
print ('Stop Words present:', tfidf_vectorizer_stop_words)

Stop Words present: None

#Sum of each word vocabulary
tf_dist_DF = np.sum(tfidf_vectorizer_features_DF, axis=0)

# For each, print the vocabulary word and the number of times it 
# appears in the training set
for tag, count in zip(tfidf_vectorizer_vocab_DF, tf_dist_DF):
    print (count, tag)

52.575448320295656 aa
3.449371988650278 aa be
2.5213942907540132 aa do
2.6920149935718545 aa flight
2.888088280018178 aadvantage
23.973326749915035 able
22.261307978187787 able to
86.01317574741131 about
3.4338285744189134 about an
7.394385569570688 about it
10.460858494348765 about my
3.0637300075569835 about our
14.596887808594984 about the
4.459667855132215 about this
6.173004512768676 about to
4.027886856443812 about you
5.752248075564953 about your
5.285452110687755 above
2.778535417165819 abq
6.83130903479284 absolute
9.952846419524633 absolutely
3.767426690977803 absurd
2.5477360071642825 ac
9.409074684611662 accept
6.295798922153386 acceptable
10.59611918045432 access
5.620252958633644 access to
7.176077498601433 accommodate
3.253859342259461 accord
3.253859342259461 accord to
18.69451177853152 account
2.9397096660528255 acct
3.016075974426048 accurate
4.771837277900692 across
5.409395845647152 act
4.084392589057226 action
5.673547995683168 actual
20.414163062911037 actually
29.39045210130111 add
4.729960644107159 add my
3.9539284122564724 add to
4.769155289800334 additional
13.444028749499553 address
3.3933554325919637 admirals
3.0517339575740308 admirals club
3.855094205294092 advance
4.605120721204297 advantage
3.655868811280402 advertise
5.449594287735075 advise
6.489806502737843 advisory
4.39505137223961 affect
3.300725672818057 afford
75.80074978923756 after
4.09014306238322 after an
5.930409110611301 after be
3.6183296648701893 after hours
3.4341812628499393 after land
5.094440859916791 after my
6.664915975203933 after the
3.729029125247926 after wait
8.557328675830776 afternoon
69.98059535054557 again
3.3821079807689487 again for
3.4702825175280805 again you
4.622212391051639 age
46.86490223824885 agent
4.0163577781490165 agent at
3.702330071099563 agent be
2.650113912471549 agent in
2.719612256829688 agent on
31.38954987259624 agents
3.9111042319472453 agents at
4.663734381072144 agents be
26.908307811180514 ago
3.9072030967074043 ago and
2.7457323599161865 ago be
2.764068855498401 agree
4.133966114558024 ah
5.182640385034744 ahead
32.45896200541834 air
8.801364194034418 aircraft
3.4734322770384196 airfare
72.87546267526605 airline
7.5035806659932485 airline be
6.841714071984906 airline ever
3.9811140816654045 airline have
5.601661667758738 airline in
3.9072808533917875 airline that
4.652098973623018 airline to
35.71850526055673 airlines
3.9466510375959594 airlines be
6.5805589498544625 airplane
61.06043247397446 airport
5.9933080298742905 airport and
5.872378277718408 airport be
4.680183221588637 airport for
4.064626925375737 airport http
2.8359045450695644 airport in
2.700879499508426 airport to
6.035677365234539 airports
22.33360246595964 airways
4.72508308275024 alert
3.4437603520115503 alist
108.73096812071249 all
8.177820592972893 all be
10.649564797786699 all day
5.624211795369766 all flight
3.69827402525842 all get
4.455756819462274 all good
3.920353140896491 all have
5.39314937206268 all my
2.743376155332574 all night
9.03689151927938 all of
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18.154906207097863 wife
5.124808494228829 wife and
29.846266911701388 wifi
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12.217112921540615 win
7.391574952214549 window
4.1579408154820685 winners
9.02966792490423 winter
13.455526704195597 wish
2.8510271895811505 wish you
191.9667674729534 with
2.7731887608176997 with aa
4.458958269282804 with all
8.266913720834074 with an
2.5766452602833603 with another
6.669136151485798 with customer
2.919764096004248 with delta
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2.9543038331590816 with kid
6.053703018279269 with me
15.00935000522657 with my
3.68918072875256 with new
20.612438263044364 with no
4.383464850138688 with our
3.769968971673311 with someone
4.962797811520801 with that
29.00200432645317 with the
7.123677699578479 with them
7.978093310980062 with this
5.544167794387018 with unite
4.150198309247161 with us
32.214443479586414 with you
14.034860730813799 with your
4.56846408705975 within
23.156550574702624 without
6.6786280341564 wof
4.061972972983005 woman
10.83664827675973 wonder
4.087934891772692 wonder if
8.568187763197075 wonderful
12.654132939359059 word
73.99698913462902 work
4.651438573734932 work and
2.9563136815754065 work at
5.6146945571044915 work for
10.3773663019739 work on
5.259218087335901 work out
5.512704496239655 work with
3.47559299049737 workers
13.054216707823779 world
14.405060296524562 worry
14.619632227472952 worse
47.63669943700006 worst
12.742396015602925 worst airline
11.393232229234886 worst customer
5.459202970572419 worst experience
3.8800754811183467 worst service
8.304158475957506 worth
110.0191327704198 would
3.755713437873732 would appreciate
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16.64450511002066 would have
2.971448748036696 would help
3.4088012138371315 would it
14.810300074374155 would like
12.20177801188268 would love
2.557761695604241 would make
16.504103264859122 would not
4.7201634827130885 would rather
2.652830724829826 would think
6.379880968301355 would you
2.671349262335154 wout
14.443886951337923 wow
9.031681201303245 write
17.588514202286163 wrong
3.5359856656948696 wrong with
5.249267330591475 wsj
10.141767806532616 wtf
2.975095363281808 wyou
4.469537509776771 ya
4.89465619892071 yay
4.231000916936426 yea
12.21287745281981 yeah
23.580945627133225 year
5.726575748786925 year old
12.12055871932438 years
4.349573046187061 yell
3.5054631595795636 yell at
5.8208897133586115 yep
55.067581121706404 yes
6.14873488231762 yes but
4.12841033234766 yes do
3.693452463460135 yes it
6.696798250816844 yes please
3.8459311478147526 yes thank
23.746075184672776 yesterday
4.296968607274318 yesterday and
2.9036204683601556 yesterday be
32.60600011944273 yet
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3.3174565432914203 york
453.2678685000618 you
10.198420559847314 you again
26.971705798271756 you all
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2.9907209850885845 you ask
2.5410586938995254 you back
70.26695515755516 you be
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40.84027756746161 you for
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62.965558491334065 you guy
59.131993766646296 you have
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3.7344539589977677 you http
2.6712080579334194 you if
3.973703563379844 you in
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18.397082952566592 you know
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2.7758685180106593 you might
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4.806929136341276 you now
3.1812437205042965 you offer
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3.7619463636164276 you pay
14.592783474952414 you please
3.2044471506587935 you provide
4.026598709383429 you put
5.380422692010959 you really
7.411658049495925 you say
4.88683272209206 you send
18.37625980122512 you should
9.709505755238567 you so
2.9731376162224916 you still
6.6705467136241365 you suck
5.735177636197063 you take
7.092488078415485 you tell
5.760146361381639 you thank
6.730682652850244 you the
6.689915106968622 you think
14.411590202661843 you to
3.514037374074288 you treat
2.9811255812692687 you use
3.424675431631356 you very
8.660659528929989 you want
3.5647716451936513 you we
22.219983413135783 you will
11.41608884529553 you would
8.289630035052273 you you
2.7595159224047645 young
214.7818425072424 your
5.404264338908236 your agents
15.89357145944946 your airline
5.272250295675639 your app
4.4813275902509115 your baggage
3.47918101703769 your call
6.778029628082597 your company
4.759897981473204 your crew
21.359143043386787 your customer
9.389359530336483 your customers
7.077178211568764 your employees
5.185752474689548 your fault
16.41498088278399 your flight
4.661337944747313 your gate
8.260933957924241 your help
3.620227789765324 your new
3.9181598888585274 your own
2.9455686178153773 your passengers
3.1560043394004382 your people
9.182432602585415 your phone
5.859517184319543 your plan
3.6311759012565155 your plane
4.54148781368738 your policy
3.687680705245189 your reservation
2.556325352554013 your reservations
4.1751463606898325 your response
7.740717293953495 your service
7.691996608345957 your site
11.239116430086465 your staff
4.733235984357473 your system
6.491991757931137 your team
15.225479412115904 your website
2.9927907947769867 your wifi
8.752693544560838 yr
4.153517938680994 yr old
3.2248663532893884 yrs
3.681216649900506 yup
3.9317596390182916 yyz
9.423992395506616 zero
2.898146714931568 zone

5) Fit and evaluate model using both type of vectorization. (6+6 Marks)¶

#RandomForest Classifier on CountVectorizer
y = TW_DF_Update['airline_sentiment']
plt.figure(figsize=(14,6))
y.value_counts().plot.barh()
plt.xlabel('Count')
plt.ylabel('Airline Sentiment').set_rotation(90)
plt.yticks(rotation=0)
plt.show()
print(y.value_counts())
print()
print('Note: The data does not seems to be balanced, lots of negative sentiments')

negative    9140
neutral     3083
positive    2345
Name: airline_sentiment, dtype: int64

Note: The data does not seems to be balanced, lots of negative sentiments

#Splitting data to train and test

x = vectorizer_features_DF
y = TW_DF_Update['airline_sentiment']
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)

print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)

(10197, 5000)
(10197,)
(4371, 5000)
(4371,)

#As mentioned in earlier step, the data is not balanced, therefore applying SMOTE (Synthetic Minority Oversampling TEchnique)
smote_DF = SMOTE(random_state=0)
X_train_SMOTE, y_train_SMOTE = smote_DF.fit_sample(x_train, y_train)

print(X_train_SMOTE.shape)
print(y_train_SMOTE.shape)

(19266, 5000)
(19266,)

y_train_SMOTE

0        negative
1        negative
2        positive
3        negative
4        negative
           ...   
19261    positive
19262    positive
19263    positive
19264    positive
19265    positive
Name: airline_sentiment, Length: 19266, dtype: object

#Checking the tweet data set is balanced after applying SMOTE (Synthetic Minority Oversampling TEchnique)
after_smote_airline_sentiment = pd.DataFrame(y_train_SMOTE, columns=['airline_sentiment'])
y = after_smote_airline_sentiment['airline_sentiment']

plt.figure(figsize=(14,6))
y.value_counts().plot.barh()
plt.xlabel('Count')
plt.ylabel('Airline Sentiment').set_rotation(90)
plt.yticks(rotation=0)
plt.show()

print(y.value_counts())
print()
print('Note: Now we can see that after applying SMOTE, the data is balanced')

neutral     6422
negative    6422
positive    6422
Name: airline_sentiment, dtype: int64

Note: Now we can see that after applying SMOTE, the data is balanced

#Splitting test data set to test and validation data
#Splitting the test data into test and validation set in 50-50 % ratio

x_validation, x_test_main, y_validation, y_test_main = train_test_split(x_test, y_test, test_size=0.50, random_state=1)

print(x_validation.shape)
print(x_test_main.shape)

print(y_validation.shape)
print(y_test_main.shape)

(2185, 5000)
(2186, 5000)
(2185,)
(2186,)

#Initialize a Random Forest classifier with 100 trees

randomforestclassifier = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,criterion='gini', 
                       max_depth=None, max_features='auto',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, n_estimators=100,
                       n_jobs=None, oob_score=False, random_state=1, verbose=0, warm_start=False) 

randomforestclassifier = randomforestclassifier.fit( X_train_SMOTE, y_train_SMOTE)

print('Random Forest Classifier Score:', randomforestclassifier.score(X_train_SMOTE, y_train_SMOTE))
print('Cross validation Score:',np.mean(cross_val_score(randomforestclassifier,X_train_SMOTE, y_train_SMOTE,cv=10)))

Random Forest Classifier Score: 0.9396345894321603
Cross validation Score: 0.8034481039779576

#Class prediction for the Validation data set
y_validation_predict= randomforestclassifier.predict(x_validation)

#Training and Validation Accuracy
print("Validation accuracy",randomforestclassifier.score(x_validation, y_validation))
print("Training accuracy",randomforestclassifier.score(X_train_SMOTE,y_train_SMOTE))

Validation accuracy 0.7670480549199085
Training accuracy 0.9396345894321603

#Classification report summary for Validation Set
print(metrics.classification_report(y_validation,y_validation_predict))

              precision    recall  f1-score   support

    negative       0.81      0.91      0.86      1331
     neutral       0.61      0.50      0.55       467
    positive       0.74      0.60      0.66       387

    accuracy                           0.77      2185
   macro avg       0.72      0.67      0.69      2185
weighted avg       0.76      0.77      0.76      2185

#Confusion Matrix for Validation Set
cm_DF = confusion_matrix(y_validation_predict, y_validation)
plt.figure(figsize=(8,8))
sns.heatmap(cm_DF, annot=True,fmt='.0f',cbar=False)
plt.title('Confusion Matrix', fontsize=15)
plt.xticks(range(3), ['Negative', 'Neutral', 'Positive'])
plt.yticks(range(3), ['Negative', 'Neutral', 'Positive'],rotation=0)
plt.show()

#Testing Data Accuracy
print("Testing accuracy:",randomforestclassifier.score(x_test_main, y_test_main))

Testing accuracy: 0.7634949679780421

#Classification report summary for Test Set
y_test_predict= randomforestclassifier.predict(x_test_main)
print(metrics.classification_report(y_test_main,y_test_predict))

              precision    recall  f1-score   support

    negative       0.83      0.90      0.86      1387
     neutral       0.61      0.49      0.54       465
    positive       0.65      0.60      0.62       334

    accuracy                           0.76      2186
   macro avg       0.69      0.66      0.67      2186
weighted avg       0.75      0.76      0.76      2186

#Confusion Matrix for Test Set
cm_DF = confusion_matrix(y_test_predict, y_test_main)
plt.figure(figsize=(8,8))
sns.heatmap(cm_DF, annot=True,fmt='.0f',cbar=False)
plt.title('Confusion Matrix', fontsize=15)
plt.xticks(range(3), ['Negative', 'Neutral', 'Positive'])
plt.yticks(range(3), ['Negative', 'Neutral', 'Positive'],rotation=0)
plt.show()

#Random Forest Classifier on TfidfVectorizer
#Splitting data set into train and Test
x_tf_idf = tfidf_vectorizer_features_DF
y_tf_idf = TW_DF_Update['airline_sentiment']
x_train_tf_idf, x_test_tf_idf, y_train_tf_idf, y_test_tf_idf = train_test_split(x_tf_idf, y_tf_idf, test_size=0.3, random_state=1)

print(x_train_tf_idf.shape)
print(y_train_tf_idf.shape)
print(x_test_tf_idf.shape)
print(y_test_tf_idf.shape)

(10197, 5000)
(10197,)
(4371, 5000)
(4371,)

y = TW_DF_Update['airline_sentiment']
plt.figure(figsize=(14,6))
y.value_counts().plot.barh()
plt.xlabel('Count')
plt.ylabel('Airline Sentiment').set_rotation(90)
plt.yticks(rotation=0)
plt.show()
print(y.value_counts())
print()
print('Note: The data does not seems to be balanced, lots of negative sentiments')

negative    9140
neutral     3083
positive    2345
Name: airline_sentiment, dtype: int64

Note: The data does not seems to be balanced, lots of negative sentiments

#As mentioned in earlier step, the data is not balanced, therefore applying SMOTE (Synthetic Minority Oversampling TEchnique)
tf_idf_smt_DF = SMOTE(random_state=0)
X_train_tf_idf_SMOTE, y_train_tf_idf_SMOTE = tf_idf_smt_DF.fit_sample(x_train_tf_idf, y_train_tf_idf)

print(X_train_tf_idf_SMOTE.shape)
print(y_train_tf_idf_SMOTE.shape)

(19266, 5000)
(19266,)

#Checking the tweet data set is balanced after applying SMOTE (Synthetic Minority Oversampling TEchnique)
after_smote_airline_sentiment_tf_idf=pd.DataFrame(y_train_tf_idf_SMOTE, columns=['airline_sentiment']) 
y = after_smote_airline_sentiment_tf_idf['airline_sentiment']

plt.figure(figsize=(14,6))
y.value_counts().plot.barh()
plt.xlabel('Count')
plt.ylabel('Airline Sentiment').set_rotation(90)
plt.yticks(rotation=0)
plt.show()

print(y.value_counts())
print()
print('Note: Now we can see that after applying SMOTE, the data is balanced')

neutral     6422
negative    6422
positive    6422
Name: airline_sentiment, dtype: int64

Note: Now we can see that after applying SMOTE, the data is balanced

#Splitting test data set to test and validation data
#Splitting the test data into test and validation set in 50-50 % ratio

x_validation_tf_idf, x_test_main_tf_idf, y_validation_tf_idf, y_test_main_tf_idf = train_test_split(x_test_tf_idf, y_test_tf_idf, test_size=0.50, random_state=1)

print(x_validation_tf_idf.shape)
print(x_test_main_tf_idf.shape)

print(y_validation_tf_idf.shape)
print(y_test_main_tf_idf.shape)

(2185, 5000)
(2186, 5000)
(2185,)
(2186,)

#Initialize a Random Forest classifier with 100 trees
randomforestclassifier_tf_idf = RandomForestClassifier(verbose=1,n_jobs=-1,n_estimators = 100) 
randomforestclassifier_tf_idf = randomforestclassifier_tf_idf.fit( X_train_tf_idf_SMOTE, y_train_tf_idf_SMOTE)

[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.3s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.6s finished

randomforestclassifier_tf_idf.score(X_train_tf_idf_SMOTE, y_train_tf_idf_SMOTE)

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.1s finished

0.9977680888612063

print("Cross Validation Score:", np.mean(cross_val_score(randomforestclassifier_tf_idf,X_train_tf_idf_SMOTE, y_train_tf_idf_SMOTE,cv=10)))

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    4.9s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   13.3s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.6s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.8s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.5s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.7s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.6s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   12.1s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.4s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   12.1s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.6s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.8s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.5s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.9s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.5s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.8s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.4s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.7s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    3.8s
[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:   11.9s finished
[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s

Cross Validation Score: 0.9141071487270848

[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

#Class prediction for the Validation data set
y_validation_predict_tf_idf= randomforestclassifier_tf_idf.predict(x_validation_tf_idf)

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

#Training and Validation Accuracy
print("Validation accuracy",randomforestclassifier_tf_idf.score(x_validation_tf_idf, y_validation_tf_idf))
print("Trainig accuracy",randomforestclassifier_tf_idf.score(X_train_tf_idf_SMOTE,y_train_tf_idf_SMOTE))

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

Validation accuracy 0.7789473684210526

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s

Trainig accuracy 0.9977680888612063

[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.1s finished

#Classification report summary for Validation Set
print(metrics.classification_report(y_validation_tf_idf,y_validation_predict_tf_idf))

              precision    recall  f1-score   support

    negative       0.81      0.92      0.86      1331
     neutral       0.64      0.53      0.58       467
    positive       0.79      0.60      0.69       387

    accuracy                           0.78      2185
   macro avg       0.75      0.68      0.71      2185
weighted avg       0.77      0.78      0.77      2185

#Confusion Matrix for Validation Set
cm_DF = confusion_matrix(y_validation_predict_tf_idf, y_validation_tf_idf)
plt.figure(figsize=(8,8))
sns.heatmap(cm_DF, annot=True,fmt='.0f',cbar=False)
plt.title('Confusion Matrix', fontsize=15)
plt.xticks(range(3), ['Negative', 'Neutral', 'Positive'])
plt.yticks(range(3), ['Negative', 'Neutral', 'Positive'],rotation=0)
plt.show()

#Testing Data Accuracy
print("Test accuracy:",randomforestclassifier_tf_idf.score(x_test_main_tf_idf, y_test_main_tf_idf))

Test accuracy: 0.7712717291857274

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

#Classification report summary for Test Set
y_test_predict_tf_idf= randomforestclassifier_tf_idf.predict(x_test_main_tf_idf)
print(metrics.classification_report(y_test_main_tf_idf,y_test_predict_tf_idf))

              precision    recall  f1-score   support

    negative       0.82      0.91      0.86      1387
     neutral       0.62      0.49      0.55       465
    positive       0.72      0.60      0.65       334

    accuracy                           0.77      2186
   macro avg       0.72      0.67      0.69      2186
weighted avg       0.76      0.77      0.76      2186

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

#Confusion Matrix for Test Set
cm_DF = confusion_matrix(y_test_predict_tf_idf, y_test_main_tf_idf)
plt.figure(figsize=(8,8))
sns.heatmap(cm_DF, annot=True,fmt='.0f',cbar=False)
plt.title('Confusion Matrix', fontsize=15)
plt.xticks(range(3), ['Negative', 'Neutral', 'Positive'])
plt.yticks(range(3), ['Negative', 'Neutral', 'Positive'],rotation=0)
plt.show()

6) Summarize your understanding of the application of Various Pre-processing and Vectorization and performance of your model on this dataset. (8 Marks)¶

Summary of pre-processing steps:¶

HTML tag removal:¶

Unstructured text contains a lot of noise, particularly when extracting the data from methods like screen scraping. HTML tags are usually one of these components which do not add any value towards analyzing the text.

Contraction:¶

Contractions are words or combinations of words that are shortened by dropping letters and replacing them by an apostrophe.

Tokenization:¶

Tokenization is splitting the sentence into words.

Removing numbers:¶

Remove numbers from list of tokenized words.

Remove special characters:¶

Remove special characters from list of tokenized words.

Remove punctuation:¶

Remove punctuation from list of tokenized words.

Convert text to lower case:¶

Converting all text to the same case.

Remove empty strings:¶

Remove empty string from list of tokenized words.

Stemming¶

It is a process of transforming a word to its root form.

Lemmatization¶

On the other hand, lemmatization reduces the words to a word existing in the language.

Summary of Vectorization methods:¶

CountVectorization:¶

CountVectorizer is used to convert a collection of text documents to a vector of token counts. It also enables the pre-processing of text data prior to generating the vector representation. This functionality makes it a highly flexible feature representation module for text.

TfidfVectorizer:¶

It is a scheme to weight individual tokens. The TfidfVectorizer will tokenize documents, learn the vocabulary and inverse document frequency weightings, and allow you to encode new documents.

Summary of Model Performance:¶

Acc=[]
Mdl=[]
Acc.append(randomforestclassifier.score(x_test_main, y_test_main))
Acc.append(randomforestclassifier_tf_idf.score(x_test_main_tf_idf, y_test_main_tf_idf))
Mdl.append("CountVectorizer")
Mdl.append("TfidfVectorizer")

indx=[0,1]
plt.barh(indx,Acc)
plt.yticks(indx,Mdl,rotation=0)
plt.xlabel('Accuracy')
plt.ylabel('Model').set_rotation(0)
plt.title('Model Accuracies')

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

Text(0.5, 1.0, 'Model Accuracies')

Accuracy_CountVectorizer_DF = pd.DataFrame({'Method':['RandomForestClassfier on CountVectorizer'], 'Accuracy': randomforestclassifier.score(x_test_main, y_test_main)},index={'1'})
Accuracy_TfidfVectorizer_DF = pd.DataFrame({'Method':['RandomForestClassfier on TfidfVectorizer'], 'Accuracy': randomforestclassifier_tf_idf.score(x_test_main_tf_idf, y_test_main_tf_idf)},index={'2'})
resultsDf = pd.concat([Accuracy_TfidfVectorizer_DF, Accuracy_CountVectorizer_DF])

[Parallel(n_jobs=16)]: Using backend ThreadingBackend with 16 concurrent workers.
[Parallel(n_jobs=16)]: Done  18 tasks      | elapsed:    0.0s
[Parallel(n_jobs=16)]: Done 100 out of 100 | elapsed:    0.0s finished

print('Note: The RandomForestClassfier on TfidfVectorizer has better accuracy.')
resultsDf

Note: The RandomForestClassfier on TfidfVectorizer has better accuracy.

General Summary of tweets:¶

#Dataframe for each category
positive_DF = TW_DF['airline_sentiment'].str.contains("positive")
negative_DF = TW_DF['airline_sentiment'].str.contains("negative")
neutral_DF = TW_DF['airline_sentiment'].str.contains("neutral")

positive_tweets = TW_DF[positive_DF]
negative_tweets = TW_DF[negative_DF]
neutral_tweets = TW_DF[neutral_DF]

#Worst Airline
worst_airline = negative_tweets[['airline','airline_sentiment_confidence','negativereason']]
cnt_worst_airline = worst_airline.groupby('airline', as_index=False).count()
cnt_worst_airline.sort_values('negativereason', ascending=False)

#Best Airline
best_airline = positive_tweets[['airline','airline_sentiment_confidence']]
cnt_best_airline = best_airline.groupby('airline', as_index=False).count()
cnt_best_airline.sort_values('airline_sentiment_confidence', ascending=False)

#Negative reason
motivation = negative_tweets[['airline','negativereason']]
cnt_bad_flight_motivation = motivation.groupby('negativereason', as_index=False).count()
cnt_bad_flight_motivation.sort_values('airline', ascending=False)

Conclusion:¶

RandomForestClassifier on TfidfVectorizer has better accuracy while RandomForestClassifier on CountVectorizer has less accuracy.
Model predicts airline sentiment correctly 77% of the times.
US Airways, United Airlines and American Airlines has the greatest number of negative reviews.
Virgin America has the least number of negative reviews.
Customer service and late flight is the reason why customers are giving bad feedback on twitter.
Southwest is the best airline in terms of sentiment confidence.

	tweet_id	airline_sentiment	airline_sentiment_confidence	negativereason	negativereason_confidence	airline	airline_sentiment_gold	name	negativereason_gold	text	tweet_coord	tweet_created	tweet_location	user_timezone
0	570306133677760513	neutral	1.0000	NaN	NaN	Virgin America	NaN	cairdin	NaN	@VirginAmerica What @dhepburn said.	NaN	2015-02-24 11:35:52 -0800	NaN	Eastern Time (US & Canada)
1	570301130888122368	positive	0.3486	NaN	0.0000	Virgin America	NaN	jnardino	NaN	@VirginAmerica plus you've added commercials t...	NaN	2015-02-24 11:15:59 -0800	NaN	Pacific Time (US & Canada)
2	570301083672813571	neutral	0.6837	NaN	NaN	Virgin America	NaN	yvonnalynn	NaN	@VirginAmerica I didn't today... Must mean I n...	NaN	2015-02-24 11:15:48 -0800	Lets Play	Central Time (US & Canada)
3	570301031407624196	negative	1.0000	Bad Flight	0.7033	Virgin America	NaN	jnardino	NaN	@VirginAmerica it's really aggressive to blast...	NaN	2015-02-24 11:15:36 -0800	NaN	Pacific Time (US & Canada)
4	570300817074462722	negative	1.0000	Can't Tell	1.0000	Virgin America	NaN	jnardino	NaN	@VirginAmerica and it's a really big bad thing...	NaN	2015-02-24 11:14:45 -0800	NaN	Pacific Time (US & Canada)
5	570300767074181121	negative	1.0000	Can't Tell	0.6842	Virgin America	NaN	jnardino	NaN	@VirginAmerica seriously would pay $30 a fligh...	NaN	2015-02-24 11:14:33 -0800	NaN	Pacific Time (US & Canada)
6	570300616901320704	positive	0.6745	NaN	0.0000	Virgin America	NaN	cjmcginnis	NaN	@VirginAmerica yes, nearly every time I fly VX...	NaN	2015-02-24 11:13:57 -0800	San Francisco CA	Pacific Time (US & Canada)
7	570300248553349120	neutral	0.6340	NaN	NaN	Virgin America	NaN	pilot	NaN	@VirginAmerica Really missed a prime opportuni...	NaN	2015-02-24 11:12:29 -0800	Los Angeles	Pacific Time (US & Canada)
8	570299953286942721	positive	0.6559	NaN	NaN	Virgin America	NaN	dhepburn	NaN	@virginamerica Well, I didn't…but NOW I DO! :-D	NaN	2015-02-24 11:11:19 -0800	San Diego	Pacific Time (US & Canada)
9	570295459631263746	positive	1.0000	NaN	NaN	Virgin America	NaN	YupitsTate	NaN	@VirginAmerica it was amazing, and arrived an ...	NaN	2015-02-24 10:53:27 -0800	Los Angeles	Eastern Time (US & Canada)
10	570294189143031808	neutral	0.6769	NaN	0.0000	Virgin America	NaN	idk_but_youtube	NaN	@VirginAmerica did you know that suicide is th...	NaN	2015-02-24 10:48:24 -0800	1/1 loner squad	Eastern Time (US & Canada)
11	570289724453216256	positive	1.0000	NaN	NaN	Virgin America	NaN	HyperCamiLax	NaN	@VirginAmerica I <3 pretty graphics. so muc...	NaN	2015-02-24 10:30:40 -0800	NYC	America/New_York
12	570289584061480960	positive	1.0000	NaN	NaN	Virgin America	NaN	HyperCamiLax	NaN	@VirginAmerica This is such a great deal! Alre...	NaN	2015-02-24 10:30:06 -0800	NYC	America/New_York
13	570287408438120448	positive	0.6451	NaN	NaN	Virgin America	NaN	mollanderson	NaN	@VirginAmerica @virginmedia I'm flying your #f...	NaN	2015-02-24 10:21:28 -0800	NaN	Eastern Time (US & Canada)
14	570285904809598977	positive	1.0000	NaN	NaN	Virgin America	NaN	sjespers	NaN	@VirginAmerica Thanks!	NaN	2015-02-24 10:15:29 -0800	San Francisco, CA	Pacific Time (US & Canada)

	tweet_id	airline_sentiment	airline_sentiment_confidence	negativereason	negativereason_confidence	airline	airline_sentiment_gold	name	negativereason_gold	text	tweet_coord	tweet_created	tweet_location	user_timezone
14625	569589643487928321	positive	1.0000	NaN	NaN	American	NaN	DrCaseyJRudkin	NaN	@AmericanAir Flight 236 was great. Fantastic c...	[40.64946781, -73.76624703]	2015-02-22 12:08:48 -0800	East Coast	NaN
14626	569589460226183168	negative	1.0000	Late Flight	1.0000	American	NaN	cataattack	NaN	@AmericanAir Flight 953 NYC-Buenos Aires has b...	NaN	2015-02-22 12:08:04 -0800	Buenos Aires, Argentina	Buenos Aires
14627	569588816438169600	negative	1.0000	Cancelled Flight	1.0000	American	NaN	KristinaMeyer7	NaN	@AmericanAir Flight Cancelled Flightled, can't...	NaN	2015-02-22 12:05:30 -0800	NaN	Eastern Time (US & Canada)
14628	569588651925098496	positive	1.0000	NaN	NaN	American	NaN	jlhalldc	NaN	Thank you. “@AmericanAir: @jlhalldc Customer R...	NaN	2015-02-22 12:04:51 -0800	Washington, DC	Eastern Time (US & Canada)
14629	569588591602458624	negative	1.0000	Customer Service Issue	1.0000	American	NaN	jontgreen89	NaN	@AmericanAir How do I change my flight if the ...	NaN	2015-02-22 12:04:37 -0800	Waco, TX	Central Time (US & Canada)
14630	569588473050611712	positive	1.0000	NaN	NaN	American	NaN	Laurelinesblog	NaN	@AmericanAir Thanks! He is.	NaN	2015-02-22 12:04:09 -0800	Chapel Hill, NC	NaN
14631	569588464896876545	negative	1.0000	Bad Flight	1.0000	American	NaN	MDDavis7	NaN	@AmericanAir thx for nothing on getting us out...	NaN	2015-02-22 12:04:07 -0800	US	Eastern Time (US & Canada)
14632	569587813856841728	neutral	0.6760	NaN	0.0000	American	NaN	Chad_SMFYM	NaN	“@AmericanAir: @TilleyMonsta George, that does...	NaN	2015-02-22 12:01:31 -0800	NaN	Central Time (US & Canada)
14633	569587705937600512	negative	1.0000	Cancelled Flight	1.0000	American	NaN	RussellsWriting	NaN	@AmericanAir my flight was Cancelled Flightled...	NaN	2015-02-22 12:01:06 -0800	Los Angeles	Arizona
14634	569587691626622976	negative	0.6684	Late Flight	0.6684	American	NaN	GolfWithWoody	NaN	@AmericanAir right on cue with the delays👌	NaN	2015-02-22 12:01:02 -0800	NaN	Quito
14635	569587686496825344	positive	0.3487	NaN	0.0000	American	NaN	KristenReenders	NaN	@AmericanAir thank you we got on a different f...	NaN	2015-02-22 12:01:01 -0800	NaN	NaN
14636	569587371693355008	negative	1.0000	Customer Service Issue	1.0000	American	NaN	itsropes	NaN	@AmericanAir leaving over 20 minutes Late Flig...	NaN	2015-02-22 11:59:46 -0800	Texas	NaN
14637	569587242672398336	neutral	1.0000	NaN	NaN	American	NaN	sanyabun	NaN	@AmericanAir Please bring American Airlines to...	NaN	2015-02-22 11:59:15 -0800	Nigeria,lagos	NaN
14638	569587188687634433	negative	1.0000	Customer Service Issue	0.6659	American	NaN	SraJackson	NaN	@AmericanAir you have my money, you change my ...	NaN	2015-02-22 11:59:02 -0800	New Jersey	Eastern Time (US & Canada)
14639	569587140490866689	neutral	0.6771	NaN	0.0000	American	NaN	daviddtwu	NaN	@AmericanAir we have 8 ppl so we need 2 know h...	NaN	2015-02-22 11:58:51 -0800	dallas, TX	NaN

	tweet_id	airline_sentiment_confidence	negativereason_confidence	retweet_count
count	1.464000e+04	14640.000000	10522.000000	14640.000000
mean	5.692184e+17	0.900169	0.638298	0.082650
std	7.791112e+14	0.162830	0.330440	0.745778
min	5.675883e+17	0.335000	0.000000	0.000000
25%	5.685592e+17	0.692300	0.360600	0.000000
50%	5.694779e+17	1.000000	0.670600	0.000000
75%	5.698905e+17	1.000000	1.000000	0.000000
max	5.703106e+17	1.000000	1.000000	44.000000

	tweet_id	airline_sentiment	airline_sentiment_confidence	negativereason	negativereason_confidence	airline	airline_sentiment_gold	name	negativereason_gold	retweet_count	text	tweet_coord	tweet_created	tweet_location	user_timezone
12040	570308309682675712	negative	1.0	Customer Service Issue	1.0	American	NaN	SweeLoTmac	NaN	0	@AmericanAir why would I even consider continu...	NaN	2015-02-24 11:44:31 -0800	NaN	Quito
12059	570305051819941889	neutral	1.0	NaN	NaN	American	NaN	Chandrafaythe	NaN	0	@AmericanAir my flight got Cancelled Flightled...	NaN	2015-02-24 11:31:34 -0800	NaN	Quito
12063	570303383782989824	neutral	1.0	NaN	NaN	American	NaN	trentgillaspie	NaN	0	.@AmericanAir just disappointed with the Fligh...	NaN	2015-02-24 11:24:57 -0800	Austin, but often Denver	Mountain Time (US & Canada)
12066	570301929580048385	neutral	1.0	NaN	NaN	American	NaN	FinEdChat	NaN	0	@AmericanAir I did	NaN	2015-02-24 11:19:10 -0800	Cincinnati, Ohio	Atlantic Time (Canada)
12068	570301395141836801	negative	1.0	Late Flight	1.0	American	NaN	JoBarredaV	NaN	1	@AmericanAir r u serious?? 304min #delay with ...	NaN	2015-02-24 11:17:02 -0800	Mexico City	Central Time (US & Canada)
12069	570300915418320897	negative	1.0	Cancelled Flight	1.0	American	NaN	LancasterPattie	NaN	0	@AmericanAir You are jumping the gun and Cance...	NaN	2015-02-24 11:15:08 -0800	NaN	NaN
12074	570299252141903873	positive	1.0	NaN	NaN	American	NaN	Mtts28	NaN	0	@AmericanAir This is exactly why ill be flying...	NaN	2015-02-24 11:08:32 -0800	Virginia	Eastern Time (US & Canada)
12075	570298770136674304	negative	1.0	Customer Service Issue	1.0	American	NaN	law_econ	NaN	0	@AmericanAir This doesn't address my issue. I ...	NaN	2015-02-24 11:06:37 -0800	Newport Beach, CA	Central Time (US & Canada)
12078	570298644475346945	negative	1.0	Customer Service Issue	1.0	American	NaN	denismishin	NaN	0	@AmericanAir submitted a case to AA customer r...	NaN	2015-02-24 11:06:07 -0800	Bellevue, WA	Eastern Time (US & Canada)
12079	570298371140939776	negative	1.0	Late Flight	1.0	American	NaN	djjohnpayne	NaN	0	@AmericanAir if by near the gate you mean sitt...	[0.0, 0.0]	2015-02-24 11:05:01 -0800	Las Vegas, NV	Pacific Time (US & Canada)
12086	570296996445204480	negative	1.0	Late Flight	1.0	American	NaN	aaronmsantos	NaN	0	@AmericanAir that's 16+ extra hours of travel ...	NaN	2015-02-24 10:59:34 -0800	Brooklyn, NY and all over.	Quito
12093	570295576446808065	negative	1.0	Customer Service Issue	1.0	American	NaN	HollyKinnamon	NaN	0	@AmericanAir I have been on hold w/customer se...	NaN	2015-02-24 10:53:55 -0800	Washington, DC	Quito
12097	570292403309035520	neutral	1.0	NaN	NaN	American	NaN	pbpinftworth	NaN	0	@AmericanAir @pbpinftworth iPhone 6 64GB (not ...	[32.82813261, -97.25115941]	2015-02-24 10:41:19 -0800	DFW, TX	Central Time (US & Canada)
12099	570291157340704769	positive	1.0	NaN	NaN	American	NaN	mpresdenver	NaN	0	@AmericanAir thanks so much!	NaN	2015-02-24 10:36:22 -0800	Denver, Colorado	Mountain Time (US & Canada)
12102	570290334158225408	positive	1.0	NaN	NaN	American	NaN	oobunillaoo	NaN	0	@AmericanAir thank you for the confirmation.	NaN	2015-02-24 10:33:05 -0800	usa::fr::uk	London

	tweet_id	airline_sentiment_confidence	negativereason_confidence	retweet_count
count	1.456800e+04	14568.000000	10484.000000	14568.000000
mean	5.692131e+17	0.899675	0.636987	0.082921
std	7.773880e+14	0.163080	0.330318	0.747520
min	5.675883e+17	0.335000	0.000000	0.000000
25%	5.685551e+17	0.691975	0.360300	0.000000
50%	5.694655e+17	1.000000	0.670400	0.000000
75%	5.698859e+17	1.000000	1.000000	0.000000
max	5.703106e+17	1.000000	1.000000	44.000000

	airline_sentiment	text
0	neutral	virginamerica what dhepburn say
1	positive	virginamerica plus you have add commercials to...
2	neutral	virginamerica i do not today must mean i need ...
3	negative	virginamerica it be really aggressive to blast...
4	negative	virginamerica and it be a really big bad thing...

	Method	Accuracy
2	RandomForestClassfier on TfidfVectorizer	0.771272
1	RandomForestClassfier on CountVectorizer	0.763495

	airline	airline_sentiment_confidence	negativereason
4	United	2633	2633
3	US Airways	2263	2263
0	American	1922	1922
2	Southwest	1186	1186
1	Delta	955	955
5	Virgin America	181	181

	negativereason	airline
3	Customer Service Issue	2898
7	Late Flight	1655
1	Can't Tell	1190
2	Cancelled Flight	839
8	Lost Luggage	718
0	Bad Flight	580
6	Flight Booking Problems	529
5	Flight Attendant Complaints	479
9	longlines	178
4	Damaged Luggage	74